1. Guest Editorial: Learning in Nonstationary and Evolving Environments
Author(s): Robi Polikar; Cesare Alippi
Pages: 9 - 11
2. COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data
Author(s): Karl B. Dyer; Robert Capo; Robi Polikar
Pages: 12 - 26
3. Active Learning With Drifting Streaming Data
Author(s): Indre Zliobaite; Albert Bifet; Bernhard Pfahringer; Geoffrey Holmes
Pages: 27 - 39
4. Online Bayesian Learning With Natural Sequential Prior Distribution
Author(s): Yohei Nakada; Makio Wakahara; Takashi Matsumoto
Pages: 40 - 54
5. PANFIS: A Novel Incremental Learning Machine
Author(s): Mahardhika Pratama; Sreenatha. G. Anavatti; Plamen. P. Angelov; Edwin Lughofer
Pages: 55 - 68
6. PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data
Author(s): Ludmila I. Kuncheva; William J. Faithfull
Pages: 69 - 80
7. Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
Author(s): Dariusz Brzezinski; Jerzy Stefanowski
Pages: 81 - 94
8. Mining Recurring Concepts in a Dynamic Feature Space
Author(s): Joao Bartolo Gomes; Mohamed Medhat Gaber; Pedro A. C. Sousa; Ernestina Menasalvas
Pages: 95 - 110
9. Dynamic Learning From Adaptive Neural Network Control of a Class of Nonaffine Nonlinear Systems
Author(s): Shi-Lu Dai; Cong Wang; Min Wang
Pages: 111 - 123
10. Learning in the Model Space for Cognitive Fault Diagnosis
Author(s): Huanhuan Chen; Peter Tino; Ali Rodan; Xin Yao
Pages: 124 - 136
11. Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems
Author(s): Vasso Reppa; Marios M. Polycarpou; Christos G. Panayiotou
Pages: 137 - 153
12. Dealing With Concept Drifts in Process Mining
Author(s): R. P. Jagadeesh Chandra Bose; Wil M. P. van der Aalst; Indre Zliobaite; Mykola Pechenizkiy
Pages: 154 - 171
13. Adaptive Convex Combination Approach for the Identification of Improper Quaternion Processes
Author(s): Bukhari Che Ujang; Cyrus Jahanchahi; Clive Cheong Took; Danilo P. Mandic
Pages: 172 - 182
14. Developmental Perception of the Self and Action
Author(s): Ryo Saegusa; Giorgio Metta; Giulio Sandini; Lorenzo Natale
Pages: 183 - 202
15. Linguistic Decision Making for Robot Route Learning
Author(s): Hongmei He; Thomas Martin McGinnity; Sonya Coleman; Bryan Gardiner
Pages: 203 - 215
16. An Interval Type-2 Neural Fuzzy Chip With On-Chip Incremental Learning Ability for Time-Varying Data Sequence Prediction and System Control
Author(s): Chia-Feng Juang; Chi-You Chen
Pages: 216 - 228
17. Learning Geotemporal Nonstationary Failure and Recovery of Power Distribution
Author(s): Yun Wei; Chuanyi Ji; Floyd Galvan; Stephen Couvillon; George Orellana; James Momoh
Pages: 229 - 240
18. Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series
Author(s): Domingos Savio Pereira Salazar; Paulo Jorge Leitao Adeodato; Adrian Lucena Arnaud
Pages: 241 - 246
Wednesday, January 1, 2014
Monday, December 23, 2013
Neural Networks Volume 50 Pages 1-182 February 2014
Neural Networks Letters
1. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networksPages: 98-109
Author(s): Ancai Zhang, Jianlong Qiu, Jinhua She
2. Cellular computational networks—A scalable architecture for learning the dynamics of large networked systems
Pages: 120-123
Author(s): Bipul Luitel, Ganesh Kumar Venayagamoorthy
Cognitive Science
3. Supervised orthogonal discriminant subspace projects learning for face recognitionPages: 33-46
Author(s): Yu Chen, Xiao-Hong Xu
Learning Systems
4. Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculationPages: 60-71
Author(s): Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Jorge Ribeiro, José Neves
5. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Pages: 72-78
Author(s): Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu
6. Compressed classification learning with Markov chain samples
Pages: 90-97
Author(s): Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan
7. Semi-supervised learning of class balance under class-prior change by distribution matching
Pages: 110-119
Author(s): Marthinus Christoffel du Plessis, Masashi Sugiyama
8. Robust support vector machine-trained fuzzy system
Pages: 154-165
Author(s): Yahya Forghani, Hadi Sadoghi Yazdi
9. Large-scale linear nonparallel support vector machine solver
Pages: 166-174
Author(s): Yingjie Tian, Yuan Ping
10. Finite time convergent learning law for continuous neural networks
Pages: 175-182
Author(s): Isaac Chairez
Mathematical and Computational Analysis
11. A Bayesian inverse solution using independent component analysisPages: 47-59
Author(s): Jouni Puuronen, Aapo Hyvärinen
12. A one-layer recurrent neural network for constrained nonsmooth invex optimization
Pages: 79-89
Author(s): Guocheng Li, Zheng Yan, Jun Wang
13. Pointwise probability reinforcements for robust statistical inference
Pages: 124-141
Author(s): Benoît Frénay, Michel Verleysen
14. A linear recurrent kernel online learning algorithm with sparse updates
Pages: 142-153
Author(s): Haijin Fan, Qing Song
Engineering and Applications
15. Correcting and combining time series forecastersPages: 1-11
Author(s): Paulo Renato A. Firmino, Paulo S.G. de Mattos Neto, Tiago A.E. Ferreira
16. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators
Pages: 12-32
Author(s): E. Sobhani-Tehrani, H.A. Talebi, K. Khorasani
Thursday, December 19, 2013
Work-Life Balance
A video of a panel session of two young(ish) professors, talking about work-life balance. I used to work in the same lab as Corey Bradshaw, and have even published with him. He is a straight-speaking person who says what he thinks, so you can be sure that what he says in this session is his honest opinion. That being said, I do have some conflicted feelings about this discussion.
On the one hand, I respect and envy their academic achievements. On the other hand, the sacrifices they have made to get where they are are just terrifying, and strike me as selfish. When Tanya Munro talks about dragging her two tiny babies to a conference, or skipping their end-of-year performance, or both of them talking about leaving their kids at home for the evening again, I can't help but think that that is just so much macho bullsh*t. For me, family comes first, there is no choice. My daughter is smart without being conceited, brave without being reckless, strong without being over-bearing, loving, caring and empathetic, without being clingy. She wouldn't be those things if she didn't have an extended family around her who were fully engaged in her upbringing. I'd rather be a "less-successful" academic, than risk losing what I have with her. She'll grow up soon enough, I can put more energy into my career then. Certainly I could achieve more if I sacrificed more, or if I slept a lot less, but if I worked an 80-hour week, I would die. It's as simple as that. One of the last things my father said to me, just a few days before he died, was "you're not a machine". I refuse to risk depriving my wife of her husband and my daughter of her father.
Another point at which I disagree with Corey is when he mentions telling his post-docs "Start publishing papers or I'm going to have to sack you". I think this is a poor management technique: if someone isn't performing, you as a manager must coach them to lift their game. Management by fear is a poor technique and just breeds resentment. Life may be too short to work with a*holes, but it's also too short to accumulate enemies.
The idea that everything you do should lead to an obvious paper is fine for someone who in only doing research, but personally I couldn't live without undergraduate teaching. I need the surge of energy I get from standing in front of a class and explaining complex concepts. I really love it when someone "gets it", when their face lights up with understanding. Even though I spend most of my time now in management, I'd never take another position where I wasn't teaching.
Finally, everyone's circumstances are different, so you shouldn't compare yourself to others. Tanya Munro may have met her husband during her first year of university, but I met my wife at 28, married her just before turning 30, and had my daughter, finished my PhD, and started my first post-doc at 31. I had problems with my PhD topic, I had health problems, and I had to work to support myself. Now I'm 40 and my career path is finally starting to settle down. I may have achieved less academically than them, but so have most people. What, really, does comparing myself to them achieve? Nothing, except perhaps to make me feel badly about myself.
One thing I do agree with is that you have to find your own balance, your own way. I think I've found mine, and I'm happier for it. The video is well worth the time to view it, if only to gain some perspectives from successful academics.
On the one hand, I respect and envy their academic achievements. On the other hand, the sacrifices they have made to get where they are are just terrifying, and strike me as selfish. When Tanya Munro talks about dragging her two tiny babies to a conference, or skipping their end-of-year performance, or both of them talking about leaving their kids at home for the evening again, I can't help but think that that is just so much macho bullsh*t. For me, family comes first, there is no choice. My daughter is smart without being conceited, brave without being reckless, strong without being over-bearing, loving, caring and empathetic, without being clingy. She wouldn't be those things if she didn't have an extended family around her who were fully engaged in her upbringing. I'd rather be a "less-successful" academic, than risk losing what I have with her. She'll grow up soon enough, I can put more energy into my career then. Certainly I could achieve more if I sacrificed more, or if I slept a lot less, but if I worked an 80-hour week, I would die. It's as simple as that. One of the last things my father said to me, just a few days before he died, was "you're not a machine". I refuse to risk depriving my wife of her husband and my daughter of her father.
Another point at which I disagree with Corey is when he mentions telling his post-docs "Start publishing papers or I'm going to have to sack you". I think this is a poor management technique: if someone isn't performing, you as a manager must coach them to lift their game. Management by fear is a poor technique and just breeds resentment. Life may be too short to work with a*holes, but it's also too short to accumulate enemies.
The idea that everything you do should lead to an obvious paper is fine for someone who in only doing research, but personally I couldn't live without undergraduate teaching. I need the surge of energy I get from standing in front of a class and explaining complex concepts. I really love it when someone "gets it", when their face lights up with understanding. Even though I spend most of my time now in management, I'd never take another position where I wasn't teaching.
Finally, everyone's circumstances are different, so you shouldn't compare yourself to others. Tanya Munro may have met her husband during her first year of university, but I met my wife at 28, married her just before turning 30, and had my daughter, finished my PhD, and started my first post-doc at 31. I had problems with my PhD topic, I had health problems, and I had to work to support myself. Now I'm 40 and my career path is finally starting to settle down. I may have achieved less academically than them, but so have most people. What, really, does comparing myself to them achieve? Nothing, except perhaps to make me feel badly about myself.
One thing I do agree with is that you have to find your own balance, your own way. I think I've found mine, and I'm happier for it. The video is well worth the time to view it, if only to gain some perspectives from successful academics.
Wednesday, December 18, 2013
WCCI 2014 Deadline Extension
The deadline for submitting papers to the World Congress on Computational Intelligence (WCCI) 2014 has been extended to the 20th of January, 2014. There will be no further extensions. This conference combines the three major conferences of the IEEE Computational Intelligence Society: The International Joint Conference on Neural Networks (IJCNN); the Congress on Evolutionary Computation (CEC) and the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). This conference will be held in Beijing, China, July 6-11, 2014.
Monday, December 16, 2013
Reminder: paper submission deadline for EAIS 2014
A reminder that the deadline for submitting papers to the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) 2014 is January 15, 2014. This conference will be held in Linz, Austria, June 2-4, 2014.
Friday, December 13, 2013
Reminder: paper submission deadline for IEEE SSCI 2014
A reminder that the deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (SSCI) 2014 is 15 June 2014. This group of symposia will be held in Orlando, Florida, 9-12 December, 2014.
Tuesday, December 10, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 January 2014.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Friday, December 6, 2013
IEEE Transactions on Fuzzy Systems, Volume 21, Number 6, December 2013
1. An Improved Estimation Method for Unmodeled Dynamics Based on ANFIS and Its Application to Controller Design
Author(s): Zhang, Y. ; Chai, T. ; Wang, H. ; Chen, X. ; Su, C.-Y.
Pages: 989-1005
2. Linguistic Computational Model Based on 2-Tuples and Intervals
Author(s): Dong, Y. ; Zhang, G. ; Hong, W.-C. ; Yu, S.
Pages: 1006-1018
3. Dynamic Fuzzy Clustering and Its Application in Motion Segmentation
Author(s): Nguyen, T.M. ; Wu, Q.M.J.
Pages: 1019-1031
4. Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification
Author(s): Luo, M. ; Sun, F. ; Liu, H.
Pages: 1032-1043
5. Intelligent Hybrid Control System Design for Antilock Braking Systems Using Self-Organizing Function-Link Fuzzy Cerebellar Model Articulation Controller
Author(s): Lin, C.-M. ; Li, H.-Y.
Pages: 1044-1055
6. Simplified Interval Type-2 Fuzzy Logic Systems
Author(s): Mendel, J.M. ; Liu, X.
Pages: 1056-1069
7. Statistical Inference of Rough Set Dependence and Importance Analysis
Author(s): Hu, D. ; Yu, X.
Pages: 1070-1079
8. A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems
Author(s): Subramanian, K. ; Suresh, S. ; Sundararajan, N.
Pages: 1080-1095
9. Strongest Strong Cycles and $theta$-Fuzzy Graphs
Author(s): Mathew, S. ; Sunitha, M.S.
Pages: 1096-1104
10. Functional Machine With Takagi–Sugeno Inference to Coordinated Movement in Underwater Vehicle-Manipulator Systems
Author(s): dos Santos, C.H.F. ; De Pieri, E.R.
Pages: 1105-1114
11. Human Reliability Evaluation for Offshore Platform Musters Using Intuitionistic Fuzzy Sets
Author(s): Tyagi, S.K. ; Akram, M.
Pages: 1115-1122
12. Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence
Author(s): Pan, Y. ; Er, M.J.
Pages: 1123-1132
13. FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility
Author(s): Pancho, D.P. ; Alonso, J.M. ; Cordon, O. ; Quirin, A. ; Magdalena, L.
Pages: 1133-1149
14. A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
Author(s): Bustince, H. ; Galar, M. ; Bedregal, B. ; Kolesarova, A. ; Mesiar, R.
Pages: 1150-1162
15. Defuzzification Functionals of Ordered Fuzzy Numbers
Author(s): Kosinski, W. ; Prokopowicz, P. ; Rosa, A.
Pages: 1163-1169
16. A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
Author(s): Havens, T.C. ; Bezdek, J.C. ; Leckie, C. ; Ramamohanarao, K. ; Palaniswami, M.
Pages: 1170-1175
Author(s): Zhang, Y. ; Chai, T. ; Wang, H. ; Chen, X. ; Su, C.-Y.
Pages: 989-1005
2. Linguistic Computational Model Based on 2-Tuples and Intervals
Author(s): Dong, Y. ; Zhang, G. ; Hong, W.-C. ; Yu, S.
Pages: 1006-1018
3. Dynamic Fuzzy Clustering and Its Application in Motion Segmentation
Author(s): Nguyen, T.M. ; Wu, Q.M.J.
Pages: 1019-1031
4. Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification
Author(s): Luo, M. ; Sun, F. ; Liu, H.
Pages: 1032-1043
5. Intelligent Hybrid Control System Design for Antilock Braking Systems Using Self-Organizing Function-Link Fuzzy Cerebellar Model Articulation Controller
Author(s): Lin, C.-M. ; Li, H.-Y.
Pages: 1044-1055
6. Simplified Interval Type-2 Fuzzy Logic Systems
Author(s): Mendel, J.M. ; Liu, X.
Pages: 1056-1069
7. Statistical Inference of Rough Set Dependence and Importance Analysis
Author(s): Hu, D. ; Yu, X.
Pages: 1070-1079
8. A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems
Author(s): Subramanian, K. ; Suresh, S. ; Sundararajan, N.
Pages: 1080-1095
9. Strongest Strong Cycles and $theta$-Fuzzy Graphs
Author(s): Mathew, S. ; Sunitha, M.S.
Pages: 1096-1104
10. Functional Machine With Takagi–Sugeno Inference to Coordinated Movement in Underwater Vehicle-Manipulator Systems
Author(s): dos Santos, C.H.F. ; De Pieri, E.R.
Pages: 1105-1114
11. Human Reliability Evaluation for Offshore Platform Musters Using Intuitionistic Fuzzy Sets
Author(s): Tyagi, S.K. ; Akram, M.
Pages: 1115-1122
12. Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence
Author(s): Pan, Y. ; Er, M.J.
Pages: 1123-1132
13. FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility
Author(s): Pancho, D.P. ; Alonso, J.M. ; Cordon, O. ; Quirin, A. ; Magdalena, L.
Pages: 1133-1149
14. A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
Author(s): Bustince, H. ; Galar, M. ; Bedregal, B. ; Kolesarova, A. ; Mesiar, R.
Pages: 1150-1162
15. Defuzzification Functionals of Ordered Fuzzy Numbers
Author(s): Kosinski, W. ; Prokopowicz, P. ; Rosa, A.
Pages: 1163-1169
16. A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
Author(s): Havens, T.C. ; Bezdek, J.C. ; Leckie, C. ; Ramamohanarao, K. ; Palaniswami, M.
Pages: 1170-1175
Thursday, December 5, 2013
IEEE Transactions on Evolutionary Computation, Volume 17, Number 6, December 2013
1. Striking a Mean- and Parent-Centric Balance in Real-Valued Crossover Operators
Author(s): Someya, H.
Pages: 737-754
2. Multiobjective Particle Swarm Optimization With Preference-Based Sort and Its Application to Path Following Footstep Optimization for Humanoid Robots
Author(s): Lee, K.-B. ; Kim, J.-H.
Pages: 755-766
3. An Energy-Based Sampling Technique for Multi-Objective Restricted Boltzmann Machine
Author(s): Shim, V.A. ; Tan, K.C. ; Cheong, C.Y.
Pages: 767-785
4. An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization
Author(s): Liu, B. ; Zhang, Q. ; Fernandez, F.V. ; Gielen, G.G.E.
Pages: 786-796
5. Scaling Up Estimation of Distribution Algorithms for Continuous Optimization
Author(s): Dong, W. ; Chen, T. ; Tino, P. ; Yao, X.
Pages: 797-822
6. Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
Author(s): Garcia-Nieto, J. ; Olivera, A.C. ; Alba, E.
Pages: 823-839
7. Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
Author(s): Sabar, N.R. ; Ayob, M. ; Kendall, G. ; Qu, R.
Pages: 840-861
8. Fitness Modeling With Markov Networks
Author(s): Brownlee, A.E.I. ; McCall, J.A.W. ; Zhang, Q.
Pages: 862-879
Author(s): Someya, H.
Pages: 737-754
2. Multiobjective Particle Swarm Optimization With Preference-Based Sort and Its Application to Path Following Footstep Optimization for Humanoid Robots
Author(s): Lee, K.-B. ; Kim, J.-H.
Pages: 755-766
3. An Energy-Based Sampling Technique for Multi-Objective Restricted Boltzmann Machine
Author(s): Shim, V.A. ; Tan, K.C. ; Cheong, C.Y.
Pages: 767-785
4. An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization
Author(s): Liu, B. ; Zhang, Q. ; Fernandez, F.V. ; Gielen, G.G.E.
Pages: 786-796
5. Scaling Up Estimation of Distribution Algorithms for Continuous Optimization
Author(s): Dong, W. ; Chen, T. ; Tino, P. ; Yao, X.
Pages: 797-822
6. Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
Author(s): Garcia-Nieto, J. ; Olivera, A.C. ; Alba, E.
Pages: 823-839
7. Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
Author(s): Sabar, N.R. ; Ayob, M. ; Kendall, G. ; Qu, R.
Pages: 840-861
8. Fitness Modeling With Markov Networks
Author(s): Brownlee, A.E.I. ; McCall, J.A.W. ; Zhang, Q.
Pages: 862-879
Neural Networks Volume 48 Pages 1-208
Neural Networks Letters
1. Is mutual information adequate for feature selection in regression?Pages: 1-7
Author(s): Benoît Frénay, Gauthier Doquire, Michel Verleysen
2. Neural architecture design based on extreme learning machine
Pages: 19-24
Author(s): Andrés Bueno-Crespo, Pedro J. García-Laencina, José-Luis Sancho-Gómez
3. Comments on the “No-Prop” algorithm
Pages: 59-60
Author(s): Meng-Hiot Lim
4. Exponential stabilization of delayed recurrent neural networks: A state estimation based approach
Pages: 153-157
Author(s): He Huang, Tingwen Huang, Xiaoping Chen, Chunjiang Qian
Neuroscience
5. A model of task-specific focal dystoniaPages: 25-31
Author(s): Eckart Altenmüller, Dieter Müller
6. Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays
Pages: 195-203
Author(s): Shiping Wen, Gang Bao, Zhigang Zeng, Yiran Chen, Tingwen Huang
Learning Systems
7. An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completionPages: 8-18
Author(s): Yuanyuan Liu, L.C. Jiao, Fanhua Shang, Fei Yin, F. Liu
8. Analysis of programming properties and the row–column generation method for 1-norm support vector machines
Pages: 32-43
Author(s): Li Zhang, WeiDa Zhou
9. Fully corrective boosting with arbitrary loss and regularization
Pages: 44-58
Author(s): Chunhua Shen, Hanxi Li, Anton van den Hengel
10. Fixed-final-time optimal control of nonlinear systems with terminal constraints
Pages: 61-71
Author(s): Ali Heydari, S.N. Balakrishnan
11. Solving graph data issues using a layered architecture approach with applications to web spam detection
Pages: 78-90
Author(s): Franco Scarselli, Ah Chung Tsoi, Markus Hagenbuchner, Lucia Di Noi
12. Fuzzy rough sets, and a granular neural network for unsupervised feature selection
Pages: 91-108
Author(s): Avatharam Ganivada, Shubhra Sankar Ray, Sankar K. Pal
13. Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule
Pages: 109-124
Author(s): Michael Beyeler, Nikil D. Dutt, Jeffrey L. Krichmar
14. 1-norm support vector novelty detection and its sparseness
Pages: 125-132
Author(s): Li Zhang, WeiDa Zhou
15. On the construction of the relevance vector machine based on Bayesian Ying-Yang harmony learning
Pages: 173-179
Author(s): Dansong Cheng, Minh Nhut Nguyen, Junbin Gao, Daming Shi
Mathematical and Computational Analysis
16. Multivariate neural network operators with sigmoidal activation functionsPages: 72-77
Author(s): Danilo Costarelli, Renato Spigler
17. Self-Organizing Hidden Markov Model Map (SOHMMM)
Pages: 133-147
Author(s): Christos Ferles, Andreas Stafylopatis
18. The breaking of a delayed ring neural network contributes to stability: The rule and exceptions
Pages: 148-152
Author(s): T.N. Khokhlova, M.M. Kipnis
19. Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays
Pages: 158-172
Author(s): Zhenyuan Guo, Jun Wang, Zheng Yan
20. Dynamical behaviors for discontinuous and delayed neural networks in the framework of Filippov differential inclusions
Pages: 180-194
Author(s): Lihong Huang, Zuowei Cai, Lingling Zhang, Lian Duan
Letter to the Editor
21. Reply to the Comments on the “No-Prop” algorithmPages: 204
Author(s): Bernard Widrow
22. A comment on “ image -stability and image -synchronization for fractional-order neural networks”
Pages: 207-208
Author(s): Li Kexue, Peng Jigen, Gao Jinghuai
Erratum
23. Erratum to “Comments on the ‘No-Prop’ algorithm” [Neural Netw. 48 (2013) 59–60]Pages: 205
Author(s): Meng-Hiot Lim
Corrigendum
24. Corrigendum to “Noise enhanced clustering and competitive learning algorithms” [Neural Netw. 37 (2013) 132–140]Pages: 206
Author(s): Osonde Osoba, Bart Kosko
Wednesday, December 4, 2013
Neural Networks new articles 25 November - 1 December, 2013
1. Feature selection and multi-kernel learning for sparse representation on a manifold
Jim Jing-Yan Wang, Halima Bensmail, Xin Gao
2. Large-scale linear Nonparallel Support Vector Machine solver
Yingjie Tian, Yuan Ping
3. Neural network for solving convex quadratic bilevel programming problems
Xing He, Chuandong Li, Tingwen Huang, Chaojie Li
Jim Jing-Yan Wang, Halima Bensmail, Xin Gao
2. Large-scale linear Nonparallel Support Vector Machine solver
Yingjie Tian, Yuan Ping
3. Neural network for solving convex quadratic bilevel programming problems
Xing He, Chuandong Li, Tingwen Huang, Chaojie Li
Thursday, November 28, 2013
Neural Networks new articles 19-25 November, 2013
1. Compressed classification learning with Markov chain samples
Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan
2. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks
Ancai Zhang, Jianlong Qiu, Jinhua She
3. Pointwise probability reinforcements for robust statistical inference
Benoît Frénay, Michel Verleysen
4. Robust support vector machine-trained fuzzy system
Yahya Forghani, Hadi Sadoghi Yazdi
5. A linear recurrent kernel online learning algorithm with sparse updates
Haijin Fan, Qing Song
6. A one-layer recurrent neural network for constrained nonsmooth invex optimization
Guocheng Li, Zheng Yan, Jun Wang
7. Semi-supervised learning of class balance under class-prior change by distribution matching
Marthinus Christoffel du Plessis, Masashi Sugiyama
8. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu
Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan
2. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks
Ancai Zhang, Jianlong Qiu, Jinhua She
3. Pointwise probability reinforcements for robust statistical inference
Benoît Frénay, Michel Verleysen
4. Robust support vector machine-trained fuzzy system
Yahya Forghani, Hadi Sadoghi Yazdi
5. A linear recurrent kernel online learning algorithm with sparse updates
Haijin Fan, Qing Song
6. A one-layer recurrent neural network for constrained nonsmooth invex optimization
Guocheng Li, Zheng Yan, Jun Wang
7. Semi-supervised learning of class balance under class-prior change by distribution matching
Marthinus Christoffel du Plessis, Masashi Sugiyama
8. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu
Monday, November 25, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 December 2013.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Friday, November 22, 2013
IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 12, December 2013
1. Canonical Correlation Analysis on Data With Censoring and Error Information
Author(s): Sun, J. ; Keates, S.
Pages: 1909-1919
2. Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems
Author(s): Huang, S.-C. ; Chen, B.-H.
Pages: 1920-1931
3. Recurrent Neural Collective Classification
Author(s): Monner, D.D. ; Reggia, J.A.
Pages: 1932-1943
4. Online Selective Kernel-Based Temporal Difference Learning
Author(s): Chen, X. ; Gao, Y. ; Wang, R.
Pages: 1944-1956
5. Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
Author(s): Wu, L. ; Feng, Z. ; Lam, J.
Pages: 1957-1972
6. Artificial Endocrine Controller for Power Management in Robotic Systems
Author(s): Sauze, C. ; Neal, M.
Pages: 1973-1985
7. Operator Control of Interneural Computing Machines
Author(s): Shih, M.-H. ; Tsai, F.-S.
Pages: 1986-1998
8. Multiple Graph Label Propagation by Sparse Integration
Author(s): Karasuyama, M. ; Mamitsuka, H.
Pages: 1999-2012
9. Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning
Author(s): Gao, X. ; Gao, F. ; Tao, D. ; Li, X.
Pages: 2013-2026
10. H_{\infty } State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements
Author(s): Shen, B. ; Wang, Z. ; Ding, D. ; Shu, H.
Pages: 2027-2037
11. Goal Representation Heuristic Dynamic Programming on Maze Navigation
Author(s): Ni, Z. ; He, H. ; Wen, J. ; Xu, X.
Pages: 2038-2050
12. Accelerated Canonical Polyadic Decomposition Using Mode Reduction
Author(s): Zhou, G. ; Cichocki, A. ; Xie, S.
Pages: 2051-2062
13. Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity
Author(s): Hsieh, H.-Y. ; Tang, K.-T.
Pages: 2063-2074
14. Neural Network Architecture for Cognitive Navigation in Dynamic Environments
Author(s): Villacorta-Atienza, J.A. ; Makarov, V.A.
Pages: 2075-2087
15. An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time
Author(s): Fairbank, M. ; Alonso, E. ; Prokhorov, D.
Pages: 2088-2100
16. Semisupervised Multitask Learning With Gaussian Processes
Author(s): Skolidis, G. ; Sanguinetti, G.
Pages: 2101-2112
17. Nonlinear Projection Trick in Kernel Methods: An Alternative to the Kernel Trick
Author(s): Kwak, N.
Pages: 2113-2119
Author(s): Sun, J. ; Keates, S.
Pages: 1909-1919
2. Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems
Author(s): Huang, S.-C. ; Chen, B.-H.
Pages: 1920-1931
3. Recurrent Neural Collective Classification
Author(s): Monner, D.D. ; Reggia, J.A.
Pages: 1932-1943
4. Online Selective Kernel-Based Temporal Difference Learning
Author(s): Chen, X. ; Gao, Y. ; Wang, R.
Pages: 1944-1956
5. Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
Author(s): Wu, L. ; Feng, Z. ; Lam, J.
Pages: 1957-1972
6. Artificial Endocrine Controller for Power Management in Robotic Systems
Author(s): Sauze, C. ; Neal, M.
Pages: 1973-1985
7. Operator Control of Interneural Computing Machines
Author(s): Shih, M.-H. ; Tsai, F.-S.
Pages: 1986-1998
8. Multiple Graph Label Propagation by Sparse Integration
Author(s): Karasuyama, M. ; Mamitsuka, H.
Pages: 1999-2012
9. Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning
Author(s): Gao, X. ; Gao, F. ; Tao, D. ; Li, X.
Pages: 2013-2026
10. H_{\infty } State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements
Author(s): Shen, B. ; Wang, Z. ; Ding, D. ; Shu, H.
Pages: 2027-2037
11. Goal Representation Heuristic Dynamic Programming on Maze Navigation
Author(s): Ni, Z. ; He, H. ; Wen, J. ; Xu, X.
Pages: 2038-2050
12. Accelerated Canonical Polyadic Decomposition Using Mode Reduction
Author(s): Zhou, G. ; Cichocki, A. ; Xie, S.
Pages: 2051-2062
13. Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity
Author(s): Hsieh, H.-Y. ; Tang, K.-T.
Pages: 2063-2074
14. Neural Network Architecture for Cognitive Navigation in Dynamic Environments
Author(s): Villacorta-Atienza, J.A. ; Makarov, V.A.
Pages: 2075-2087
15. An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time
Author(s): Fairbank, M. ; Alonso, E. ; Prokhorov, D.
Pages: 2088-2100
16. Semisupervised Multitask Learning With Gaussian Processes
Author(s): Skolidis, G. ; Sanguinetti, G.
Pages: 2101-2112
17. Nonlinear Projection Trick in Kernel Methods: An Alternative to the Kernel Trick
Author(s): Kwak, N.
Pages: 2113-2119
Thursday, November 21, 2013
Evolving Systems Vol 4, Issue 4, November 2013
1. Editorial: Adaptive connectionist systems for engineering applications
Author(s): Chrisina Jayne
Abstract Full text HTML Full text PDF
2. Neurocomputing techniques to dynamically forecast spatiotemporal air pollution data
Author(s): Antonios Papaleonidas & Lazaros Iliadis
Abstract Full text HTML Full text PDF
3. Information dynamics based self-adaptive reservoir for delay temporal memory tasks
Author(s): Sakyasingha Dasgupta , Florentin Wörgötter & Poramate Manoonpong
Abstract Full text HTML Full text PDF
4. EANN 2012: exploratory analysis of mobile phone traffic patterns using 1-dimensional SOM, clustering and anomaly detection
Author(s): Pekka Kumpulainen & Kimmo Hätönen
Abstract Full text HTML Full text PDF
5. Neural Adaptive Control in Application Service Management Environment
Author(s): Tomasz D. Sikora & George D. Magoulas
Abstract Full text HTML Full text PDF
Author(s): Chrisina Jayne
Abstract Full text HTML Full text PDF
2. Neurocomputing techniques to dynamically forecast spatiotemporal air pollution data
Author(s): Antonios Papaleonidas & Lazaros Iliadis
Abstract Full text HTML Full text PDF
3. Information dynamics based self-adaptive reservoir for delay temporal memory tasks
Author(s): Sakyasingha Dasgupta , Florentin Wörgötter & Poramate Manoonpong
Abstract Full text HTML Full text PDF
4. EANN 2012: exploratory analysis of mobile phone traffic patterns using 1-dimensional SOM, clustering and anomaly detection
Author(s): Pekka Kumpulainen & Kimmo Hätönen
Abstract Full text HTML Full text PDF
5. Neural Adaptive Control in Application Service Management Environment
Author(s): Tomasz D. Sikora & George D. Magoulas
Abstract Full text HTML Full text PDF
Friday, November 15, 2013
Reminder: paper submission deadline for SCDM 2014
A reminder that the deadline for submitting papers to the First International Conference on Data Mining (SCDM) 2014 is 15 December 2014. This conference will be held in Kuala Lumpur, Malaysia, 16-18 June, 2014.
Friday, November 8, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 December 2013.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Monday, November 4, 2013
IEEE Transactions on Fuzzy Systems: Volume 21, Issue 5, October 2013
1. Fuzzy-Model-Based Fault-Tolerant Design for Nonlinear Stochastic Systems Against Simultaneous Sensor and Actuator Faults
Author(s): Ming Liu ; Xibin Cao ; Peng Shi
Page(s): 789-799
2. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems Using Switching Polynomial Lyapunov Function
Author(s): Lam, H.K. ; Narimani, M. ; Hongyi Li ; Honghai Liu
Page(s): 800-813
3. Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space
Author(s): Xu-Qing Tang ; Ping Zhu
Page(s): 814-824
4. RFRR: Robust Fuzzy Rough Reduction
Author(s): Suyun Zhao ; Hong Chen ; Cuiping Li ; Mengyao Zhai ; Xiaoyong Du
Page(s): 825-841
5. Model Checking of Linear-Time Properties Based on Possibility Measure
Author(s): Yongming Li ; Lijun Li
Page(s): 842-854
6. Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means
Author(s): Izakian, H. ; Pedrycz, W. ; Jamal, I.
Page(s): 855-868
7. Conditional Density Estimation Using Probabilistic Fuzzy Systems
Author(s): van den Berg, J. ; Kaymak, U. ; Almeida, R.J.
Page(s): 869-882
8. Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach
Author(s): Lin Zhao ; Huijun Gao ; Karimi, H.R.
Page(s): 883-897
9. Multiary α-Resolution Principle for a Lattice-Valued Logic
Author(s): Yang Xu ; Jun Liu ; Xiaomei Zhong ; Shuwei Chen
Page(s): 898-912
10. Adaptive Fuzzy Decentralized Output Feedback Control for Nonlinear Large-Scale Systems With Unknown Dead-Zone Inputs
Author(s): Shaocheng Tong ; Yongming Li
Page(s): 913-925
11. Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design
Author(s): Huimin Jiang ; Kwong, C.K. ; Ip, W.H. ; Zengqiang Chen
Page(s): 926-936
12. Induction of Shadowed Sets Based on the Gradual Grade of Fuzziness
Author(s): Tahayori, H. ; Sadeghian, A. ; Pedrycz, W.
Page(s): 937-949
13. A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers
Author(s): Trawinski, K. ; Cordon, O. ; Sanchez, L. ; Quirin, A.
Page(s): 950-965
14. Network-Based Robust Passive Control for Fuzzy Systems With Randomly Occurring Uncertainties
Author(s): Zheng-Guang Wu ; Peng Shi ; Hongye Su ; Jian Chu
Page(s): 966-970
15. A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images
Author(s): Camarena, J.-G. ; Gregori, V. ; Morillas, S. ; Sapena, A.
Page(s): 971-977
16. Proximity-Based Clustering: A Search for Structural Consistency in Data With Semantic Blocks of Features
Author(s): Pedrycz, W.
Page(s): 978-982
17. A Note on Fuzzy Relational Equations With Min-Implication Composition
Author(s): Pingke Li
Page(s): 983-986
18. Comments on “Quantized Control Design for Impulsive Fuzzy Networked Systems”
Author(s): Guotao Hui ; Jun Yang ; Bonan Huang
Page(s): 987
Author(s): Ming Liu ; Xibin Cao ; Peng Shi
Page(s): 789-799
2. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems Using Switching Polynomial Lyapunov Function
Author(s): Lam, H.K. ; Narimani, M. ; Hongyi Li ; Honghai Liu
Page(s): 800-813
3. Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space
Author(s): Xu-Qing Tang ; Ping Zhu
Page(s): 814-824
4. RFRR: Robust Fuzzy Rough Reduction
Author(s): Suyun Zhao ; Hong Chen ; Cuiping Li ; Mengyao Zhai ; Xiaoyong Du
Page(s): 825-841
5. Model Checking of Linear-Time Properties Based on Possibility Measure
Author(s): Yongming Li ; Lijun Li
Page(s): 842-854
6. Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means
Author(s): Izakian, H. ; Pedrycz, W. ; Jamal, I.
Page(s): 855-868
7. Conditional Density Estimation Using Probabilistic Fuzzy Systems
Author(s): van den Berg, J. ; Kaymak, U. ; Almeida, R.J.
Page(s): 869-882
8. Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach
Author(s): Lin Zhao ; Huijun Gao ; Karimi, H.R.
Page(s): 883-897
9. Multiary α-Resolution Principle for a Lattice-Valued Logic
Author(s): Yang Xu ; Jun Liu ; Xiaomei Zhong ; Shuwei Chen
Page(s): 898-912
10. Adaptive Fuzzy Decentralized Output Feedback Control for Nonlinear Large-Scale Systems With Unknown Dead-Zone Inputs
Author(s): Shaocheng Tong ; Yongming Li
Page(s): 913-925
11. Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design
Author(s): Huimin Jiang ; Kwong, C.K. ; Ip, W.H. ; Zengqiang Chen
Page(s): 926-936
12. Induction of Shadowed Sets Based on the Gradual Grade of Fuzziness
Author(s): Tahayori, H. ; Sadeghian, A. ; Pedrycz, W.
Page(s): 937-949
13. A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers
Author(s): Trawinski, K. ; Cordon, O. ; Sanchez, L. ; Quirin, A.
Page(s): 950-965
14. Network-Based Robust Passive Control for Fuzzy Systems With Randomly Occurring Uncertainties
Author(s): Zheng-Guang Wu ; Peng Shi ; Hongye Su ; Jian Chu
Page(s): 966-970
15. A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images
Author(s): Camarena, J.-G. ; Gregori, V. ; Morillas, S. ; Sapena, A.
Page(s): 971-977
16. Proximity-Based Clustering: A Search for Structural Consistency in Data With Semantic Blocks of Features
Author(s): Pedrycz, W.
Page(s): 978-982
17. A Note on Fuzzy Relational Equations With Min-Implication Composition
Author(s): Pingke Li
Page(s): 983-986
18. Comments on “Quantized Control Design for Impulsive Fuzzy Networked Systems”
Author(s): Guotao Hui ; Jun Yang ; Bonan Huang
Page(s): 987
Friday, November 1, 2013
IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 11, November 2013
1. Error Surface of Recurrent Neural Networks
Author(s): Phan, M.C. ; Hagan, M.T.
Pages: 1709-1721
2. Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram
Author(s): Tengtrairat, N. ; Gao, B. ; Woo, W.L. ; Dlay, S.S.
Pages: 1722-1735
3. On the SVMpath Singularity
Author(s): Dai, J. ; Chang, C. ; Mai, F. ; Zhao, D. ; Xu, W.
Pages: 1736-1748
4. Multistability of Two Kinds of Recurrent Neural Networks With Activation Functions Symmetrical About the Origin on the Phase Plane
Author(s): Zeng, Z. ; Zheng, W.X.
Pages: 1749-1762
5. Safety-Aware Semi-Supervised Classification
Author(s): Wang, Y. ; Chen, S.
Pages: 1763-1772
6. Neural Network Approaches for Noisy Language Modeling
Author(s): Li, J. ; Ouazzane, K. ; Kazemian, H.B. ; Afzal, M.S.
Pages: 1773-1784
7. A New Discrete-Continuous Algorithm for Radial Basis Function Networks Construction
Author(s): Zhang, L. ; Li, K. ; He, H. ; Irwin, G.W.
Pages: 1785-1798
8. Finding Potential Support Vectors in Separable Classification Problems
Author(s): Varagnolo, D. ; Del Favero, S. ; Dinuzzo, F. ; Schenato, L. ; Pillonetto, G.
Pages: 1799-1813
9. Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks
Author(s): Fu, Z.-J. ; Xie, W.-F. ; Han, X. ; Luo, W.-D.
Pages: 1814-1823
10. Hierarchical Similarity Transformations Between Gaussian Mixtures
Author(s): Rigas, G. ; Nikou, C. ; Goletsis, Y. ; Fotiadis, D.I.
Pages: 1824-1835
11. Negative Correlation Ensemble Learning for Ordinal Regression
Author(s): Fernandez-Navarro, F. ; Gutierrez, P.A. ; Hervas-Martinez, C. ; Yao, X.
Pages: 1836-1849
12. Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference
Author(s): Fan, W. ; Bouguila, N.
Pages: 1850-1862
13. Transfer Ordinal Label Learning
Author(s): Seah, C.-W. ; Tsang, I.W. ; Ong, Y.-S.
Pages: 1863-1876
14. Pseudo-Orthogonalization of Memory Patterns for Associative Memory
Author(s): Oku, M. ; Makino, T. ; Aihara, K.
Pages: 1877-1887
15. Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits
Author(s): Ferng, C.-S. ; Lin, H.-T.
Pages: 1888-1900
16. Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation
Author(s): Gonzalez, P. ; Alvarez, E. ; Barranquero, J. ; Diez, J. ; Gonzalez-Quiros, R. ; Nogueira, E. ; Lopez-Urrutia, A. ; del Coz, J.J.
Pages: 1901-1905
17. Corrections to: “Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay”
Author(s): Zhang, D. ; Yu, L. ; Wang, Q.-G. ; Ong, C.
Pages: 1906
Author(s): Phan, M.C. ; Hagan, M.T.
Pages: 1709-1721
2. Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram
Author(s): Tengtrairat, N. ; Gao, B. ; Woo, W.L. ; Dlay, S.S.
Pages: 1722-1735
3. On the SVMpath Singularity
Author(s): Dai, J. ; Chang, C. ; Mai, F. ; Zhao, D. ; Xu, W.
Pages: 1736-1748
4. Multistability of Two Kinds of Recurrent Neural Networks With Activation Functions Symmetrical About the Origin on the Phase Plane
Author(s): Zeng, Z. ; Zheng, W.X.
Pages: 1749-1762
5. Safety-Aware Semi-Supervised Classification
Author(s): Wang, Y. ; Chen, S.
Pages: 1763-1772
6. Neural Network Approaches for Noisy Language Modeling
Author(s): Li, J. ; Ouazzane, K. ; Kazemian, H.B. ; Afzal, M.S.
Pages: 1773-1784
7. A New Discrete-Continuous Algorithm for Radial Basis Function Networks Construction
Author(s): Zhang, L. ; Li, K. ; He, H. ; Irwin, G.W.
Pages: 1785-1798
8. Finding Potential Support Vectors in Separable Classification Problems
Author(s): Varagnolo, D. ; Del Favero, S. ; Dinuzzo, F. ; Schenato, L. ; Pillonetto, G.
Pages: 1799-1813
9. Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks
Author(s): Fu, Z.-J. ; Xie, W.-F. ; Han, X. ; Luo, W.-D.
Pages: 1814-1823
10. Hierarchical Similarity Transformations Between Gaussian Mixtures
Author(s): Rigas, G. ; Nikou, C. ; Goletsis, Y. ; Fotiadis, D.I.
Pages: 1824-1835
11. Negative Correlation Ensemble Learning for Ordinal Regression
Author(s): Fernandez-Navarro, F. ; Gutierrez, P.A. ; Hervas-Martinez, C. ; Yao, X.
Pages: 1836-1849
12. Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference
Author(s): Fan, W. ; Bouguila, N.
Pages: 1850-1862
13. Transfer Ordinal Label Learning
Author(s): Seah, C.-W. ; Tsang, I.W. ; Ong, Y.-S.
Pages: 1863-1876
14. Pseudo-Orthogonalization of Memory Patterns for Associative Memory
Author(s): Oku, M. ; Makino, T. ; Aihara, K.
Pages: 1877-1887
15. Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits
Author(s): Ferng, C.-S. ; Lin, H.-T.
Pages: 1888-1900
16. Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation
Author(s): Gonzalez, P. ; Alvarez, E. ; Barranquero, J. ; Diez, J. ; Gonzalez-Quiros, R. ; Nogueira, E. ; Lopez-Urrutia, A. ; del Coz, J.J.
Pages: 1901-1905
17. Corrections to: “Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay”
Author(s): Zhang, D. ; Yu, L. ; Wang, Q.-G. ; Ong, C.
Pages: 1906
Tuesday, October 29, 2013
Prioritizing insect pests with Kohonen SOM
My research interests and activities are split between two fields: computational intelligence (obviously) and ecological modelling. Although I got into ecological modelling via computational intelligence, many of my recent publications in ecological modelling haven't had anything to do with computational intelligence. An exception to this is a recently published paper in the journal Neobiota that I am a coauthor of: "Prioritizing the risk of plant pests by clustering methods: self-organising maps, k-means and hierarchical clustering".
The problem is this: given the species that are known to exist in various geo-political regions of the world, what is the likelihood of one of those species establishing in a region where it is not already present? Species presences and absences are represented by binary vectors, where each region has a vector, a one represents a presence of a particular species in that region, and a zero represents an absence in that region. By clustering the assemblage vectors using a SOM, it is possible to infer which species pose the greatest threat to any particular region.
The rationale behind this approach is that regions that have similar species assemblages are likely to have similar environments. So if several assemblages end up in the same cluster, and a species is present in many of those regions but absent in others, then that species is likely to become established in the regions from which it is absent.
In this work the SOM were used as data clustering algorithms, with the vector quantisation abilities of the SOM being largely underutilized. My own contribution to the work was the realisation that the SOM were being used to cluster data, and hence to test the approach against the much-faster k-means clustering algorithm. I found that k-means is just as effective at producing good clusters as the SOM, and is much faster.
There are some problems with this work as well: it is virtually impossible to determine which approach is better without testing data. Which means that if you are clustering a set of species assemblages, you also need some more up-to-date data to validate the predictions. I do have some thoughts on getting around this, which I am currently investigating.
The problem is this: given the species that are known to exist in various geo-political regions of the world, what is the likelihood of one of those species establishing in a region where it is not already present? Species presences and absences are represented by binary vectors, where each region has a vector, a one represents a presence of a particular species in that region, and a zero represents an absence in that region. By clustering the assemblage vectors using a SOM, it is possible to infer which species pose the greatest threat to any particular region.
The rationale behind this approach is that regions that have similar species assemblages are likely to have similar environments. So if several assemblages end up in the same cluster, and a species is present in many of those regions but absent in others, then that species is likely to become established in the regions from which it is absent.
In this work the SOM were used as data clustering algorithms, with the vector quantisation abilities of the SOM being largely underutilized. My own contribution to the work was the realisation that the SOM were being used to cluster data, and hence to test the approach against the much-faster k-means clustering algorithm. I found that k-means is just as effective at producing good clusters as the SOM, and is much faster.
There are some problems with this work as well: it is virtually impossible to determine which approach is better without testing data. Which means that if you are clustering a set of species assemblages, you also need some more up-to-date data to validate the predictions. I do have some thoughts on getting around this, which I am currently investigating.
Friday, October 25, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 December 2013.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Wednesday, October 23, 2013
Conference paper deadline: KES 2014
The deadline for submitting papers to the 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) 2014 is 15 March, 2014. This conference will be held in Gdynia, Poland, 15-17 September, 2014.
Tuesday, October 22, 2013
Conference paper deadline: EAIS 2014
The deadline for submitting papers to the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) 2014 is January 15, 2014. This conference will be held in Linz, Austria, June 2-4, 2014.
Monday, October 21, 2013
Reminder: paper submission deadline: ICAISC 2014
A reminder that the deadline for submitting papers to the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2014 is November 20, 2013 (beware the front page linked to has embedded folk-music playing). This conference will be held in Zakopane, Poland, June 1-5 2014.
Friday, October 18, 2013
Video: How to publish your research in IEEE CIS publications
A panel discussion on how to publish in the IEEE Computational Intelligence Society's publications. This discussion wraps up the individual talks by Professors Xin Yao, Kay Chen Tan, Derong Liu, Garrison Greenwood, Chin-Teng Lin and Simon M. Lucas . This panel session was part of the CEC 2013 conference.
Thursday, October 17, 2013
How to publish your research: Video of Professor Simon M. Lucas
Professor Simon M. Lucas talks about how to publish research in IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games. This talk was part of a panel session at CEC 2013 and mostly talks about what kind of papers are published in TCIAIG.
Wednesday, October 16, 2013
How to publish your research: Video of Professor Chin-Teng Lin
Professor Chin-Teng Lin, who is the editor-in-chief of IEEE Transactions on Fuzzy Systems, speaks about publishing in that journal. This talk was part of a panel discussion at the CEC 2013 conference. Some of the points he makes in this talk are applicable to publishing in most journals:
- Read existing papers, know the field
- Present an issue of significance
- Choose a journal that fits well with your research
- Use the correct format for that journal
- Have focus & vision, don't be too ambitious with your paper
- Write clearly
- Get pre-review, ask your colleagues to check your paper before submission
- Proofread!
- Be patient, reviews take time
Tuesday, October 15, 2013
How to publish your research: Video of Professor Garrison Greenwood
Professor Garrison Greenwood talks about how to publish your research in the IEEE Transactions on Evolutionary Computation. This talk was part of a panel session at the CEC 2013 conference.
The main points of this talk are:
The main points of this talk are:
- Establish the context of your research in the Related Research section following the introduction (as a side-note, in other fields this is the introduction: why does computer science insist of separating them?
- Use enough citations, and no more
- Write enough detail for a competent researcher to replicate your work, don't try to write a tutorial
- Read the instructions for authors
- Use good grammar and spelling
Monday, October 14, 2013
How to publish your research: Video of Professor Derong Liu
Professor Derong Liu talks about how to publish your research in IEEE Transactions on Neural Networks and Learning Systems at a panel session at the CEC 2013 conference. This talk gives a lot of information about the editorial and review process, as well as how to increase your chances of having a paper accepted and even how to get on the editorial board of the journal.
Friday, October 11, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 December 2013.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Thursday, October 10, 2013
How to publish your research: Video of Professor Kay Chen Tan
A video of a talk by Professor Kay Chen Tan as part of a panel session at the CEC 2013 conference. In this video he describes the IEEE CIS Computational Intelligence Magazine.
Wednesday, October 9, 2013
How to publish your research: Video of Professor Xin Yao
This is a video of a talk given by Professor Xin Yao as part of a panel session at the CEC 2013 conference. A couple of the most salient points that I noticed:
1) If you want to publish your research, you must first do good research
2) Contact the editors of the journals you want to publish in before submitting
1) If you want to publish your research, you must first do good research
2) Contact the editors of the journals you want to publish in before submitting
Tuesday, October 8, 2013
IEEE Transactions on Computational Intelligence and AI in Games: Volume 5, Issue 3, September 2013
1. Monte Carlo Search Algorithm Discovery for Single-Player Games
Author(s): F. Maes, D. L. St-Pierre, and D. Ernst
Pages: 201-213
2. On Scalability, Generalization, and Hybridization of Coevolutionary Learning: A Case Study for Othello
Author(s): M. Szubert, W. Jaśkowski, and K. Krawiec
Pages: 214-226
3. Database-Driven Real-Time Heuristic Search in Video-Game Pathfinding
Author(s): R. Lawrence and V. Bulitko
Pages: 227-241
4. Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
Author(s): J. Li, G. Kendall, and A. V. Vasilakos
Pages: 242-250
5. An Efficient Approach to Solving Nonograms
Author(s): I.-C. Wu, D.-J. Sun, L.-P. Chen, K.-Y. Chen, C.-H. Kuo, H.-H. Kang, and H.-H. Lin
Pages: 251-264
6. Applicability of GPGPU Computing to Real-Time AI Solutions in Games
Author(s): W. Blewitt, G. Ushaw, and G. Morgan
Pages: 265-275
7. Crowdsourcing the Aesthetics of Platform Games
Author(s): N. Shaker, G. N. Yannakakis, and J. Togelius
Pages: 276-290
Author(s): F. Maes, D. L. St-Pierre, and D. Ernst
Pages: 201-213
2. On Scalability, Generalization, and Hybridization of Coevolutionary Learning: A Case Study for Othello
Author(s): M. Szubert, W. Jaśkowski, and K. Krawiec
Pages: 214-226
3. Database-Driven Real-Time Heuristic Search in Video-Game Pathfinding
Author(s): R. Lawrence and V. Bulitko
Pages: 227-241
4. Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
Author(s): J. Li, G. Kendall, and A. V. Vasilakos
Pages: 242-250
5. An Efficient Approach to Solving Nonograms
Author(s): I.-C. Wu, D.-J. Sun, L.-P. Chen, K.-Y. Chen, C.-H. Kuo, H.-H. Kang, and H.-H. Lin
Pages: 251-264
6. Applicability of GPGPU Computing to Real-Time AI Solutions in Games
Author(s): W. Blewitt, G. Ushaw, and G. Morgan
Pages: 265-275
7. Crowdsourcing the Aesthetics of Platform Games
Author(s): N. Shaker, G. N. Yannakakis, and J. Togelius
Pages: 276-290
Monday, October 7, 2013
IEEE Transactions on Autonomous Mental Development: Volume 5, Issue 3, 2013
1. GUEST EDITORIAL: Microdynamics of Interaction: Capturing and Modeling Infants’ Social Learning
Author(s): K. J. Rohlfing and G. O. Deák
Pages: 189-191
2. Mothers’ infant-directed gaze during object demonstration highlights action boundaries and goals
Author(s): R. J. Brand, E. Hollenbeck, and J. F. Kominsky
Pages: 192-201
3. From Action to Interaction: Infant Object Exploration and Mothers’ Contingent Responsiveness
Author(s): C. S. Tamis-LeMonda, Y. Kuchirko, and L. Tafuro
Pages: 202-209
4. Young Children’s Dialogical Actions: The Beginnings of Purposeful Intersubjectivity
Author(s): J. Rączaszek-Leonardi, I. Nomikou, and K.J.Rohlfing
Pages: 210-221
5. From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Nonlinguistic Sensorimotor Skills
Author(s): T. Cederborg and P.-Y. Oudeyer
Pages: 222-239
6. Supporting Early Vocabulary Development: What Sort of Responsiveness Matters?
Author(s): M. L. McGillion, J. S. Herbert, J. M. Pine, T. Keren-Portnoy, M. M. Vihman, and D. E. Matthews
Pages: 240-248
7. SEED Framework of Early Language Development: The Dynamic Coupling of Infant–Caregiver Perceiving and Acting Forms a Continuous Loop during Interaction
Author(s): P. Zukow-Goldring and N. d. V. Rader
Pages: 249-257
8. Methodological Considerations For Investigating the Microdynamics of Social Interaction Development
Author(s): K. de Barbaro, C. M. Johnson, D. Forster, and G. O. Deák
Pages: 258
Author(s): K. J. Rohlfing and G. O. Deák
Pages: 189-191
2. Mothers’ infant-directed gaze during object demonstration highlights action boundaries and goals
Author(s): R. J. Brand, E. Hollenbeck, and J. F. Kominsky
Pages: 192-201
3. From Action to Interaction: Infant Object Exploration and Mothers’ Contingent Responsiveness
Author(s): C. S. Tamis-LeMonda, Y. Kuchirko, and L. Tafuro
Pages: 202-209
4. Young Children’s Dialogical Actions: The Beginnings of Purposeful Intersubjectivity
Author(s): J. Rączaszek-Leonardi, I. Nomikou, and K.J.Rohlfing
Pages: 210-221
5. From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Nonlinguistic Sensorimotor Skills
Author(s): T. Cederborg and P.-Y. Oudeyer
Pages: 222-239
6. Supporting Early Vocabulary Development: What Sort of Responsiveness Matters?
Author(s): M. L. McGillion, J. S. Herbert, J. M. Pine, T. Keren-Portnoy, M. M. Vihman, and D. E. Matthews
Pages: 240-248
7. SEED Framework of Early Language Development: The Dynamic Coupling of Infant–Caregiver Perceiving and Acting Forms a Continuous Loop during Interaction
Author(s): P. Zukow-Goldring and N. d. V. Rader
Pages: 249-257
8. Methodological Considerations For Investigating the Microdynamics of Social Interaction Development
Author(s): K. de Barbaro, C. M. Johnson, D. Forster, and G. O. Deák
Pages: 258
Friday, October 4, 2013
IEEE Transactions on Evolutionary Computation: Volume 17, Issue 5, October 2013
1. On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization
Author(s): A. Y. S. Lam, V. O. K. Li, and J. Xu
Pages: 605-620
2. A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem
Author(s): S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan
Pages: 621-639
3. An Evolutionary Negative-Correlation Framework for Robust Analog-Circuit Design Under Uncertain Faults
Author(s): M. Liu and J. He
Pages: 640-665
4. Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection
Author(s): A. Basak, S. Das, and K. C. Tan
Pages: 666-685
5. Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers
Author(s): A. R. Baig, W. Shahzad, and S. Khan
Pages: 686-704
6. An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods
Author(s): M. Hu, T. Wu, and J. D. Weir
Pages: 705-720
7. A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Author(s): S. Yang, M. Li, X. Liu, and J. Zheng
Pages: 721
Author(s): A. Y. S. Lam, V. O. K. Li, and J. Xu
Pages: 605-620
2. A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem
Author(s): S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan
Pages: 621-639
3. An Evolutionary Negative-Correlation Framework for Robust Analog-Circuit Design Under Uncertain Faults
Author(s): M. Liu and J. He
Pages: 640-665
4. Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection
Author(s): A. Basak, S. Das, and K. C. Tan
Pages: 666-685
5. Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers
Author(s): A. R. Baig, W. Shahzad, and S. Khan
Pages: 686-704
6. An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods
Author(s): M. Hu, T. Wu, and J. D. Weir
Pages: 705-720
7. A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Author(s): S. Yang, M. Li, X. Liu, and J. Zheng
Pages: 721
Thursday, October 3, 2013
IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 10, October 2013
1. Adaptive Optimal Control of Unknown Constrained-Input Systems Using Policy Iteration and Neural Networks
Author(s): H. Modares, F. L. Lewis, and M.-B. Naghibi-Sistani
Pages: 1513-1525
2. Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition
Author(s): V. G. Kaburlasos, S. E. Papadakis, and G. A. Papakostas
Pages: 1526-1538
3. Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons
Author(s): Q. Yu, H. Tang, K. C. Tan, and H. Li
Pages: 1539-1552
4. Mean Vector Component Analysis for Visualization and Clustering of Nonnegative Data
Author(s): R. Jenssen
Pages: 1553-1564
5. RBF-Based Technique for Statistical Demodulation of Pathological Tremor
Author(s): F. Gianfelici
Pages: 1565-1574
6. Automated Induction of Heterogeneous Proximity Measures for Supervised Spectral Embedding
Author(s): E. Rodriguez-Martinez, T. Mu, J. Jiang, and J. Y. Goulermas
Pages: 1575-1587
7. Coordination of Multiagents Interacting Under Independent Position and Velocity Topologies
Author(s): J. Qin and C. Yu
Pages: 1588-1597
8. Learning Capability of Relaxed Greedy Algorithms
Author(s): S. Lin, Y. Rong, X. Sun, and Z. Xu
Pages: 1598-1608
9. Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection
Author(s): M. Tan, I. W. Tsang, and L. Wang
Pages: 1609-1622
10. Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting
Author(s): R. Coop, A. Mishtal, and I. Arel
Pages: 1623-1634
11. SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems
Author(s): X.-X. Zhang, Y. Jiang, H.-X. Li, and S.-Y. Li
Pages: 1635-1647
12. Single Image Super-Resolution With Multiscale Similarity Learning
Author(s): K. Zhang, X. Gao, D. Tao, and X. Li
Pages: 1648-1659
13. Tracking Algorithms for Multiagent Systems
Author(s): D. Meng, Y. Jia, J. Du, and F. Yu
Pages: 1660-1676
14. A Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps
Author(s): D. Wang and S. Tapan
Pages: 1677-1688
15. EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
Author(s): C.-T. Lin, S.-F. Tsai, and L.-W. Ko
Pages: 1689-1700
16. New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays
Author(s): G. Zhang and Y. Shen
Pages: 1701
Author(s): H. Modares, F. L. Lewis, and M.-B. Naghibi-Sistani
Pages: 1513-1525
2. Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition
Author(s): V. G. Kaburlasos, S. E. Papadakis, and G. A. Papakostas
Pages: 1526-1538
3. Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons
Author(s): Q. Yu, H. Tang, K. C. Tan, and H. Li
Pages: 1539-1552
4. Mean Vector Component Analysis for Visualization and Clustering of Nonnegative Data
Author(s): R. Jenssen
Pages: 1553-1564
5. RBF-Based Technique for Statistical Demodulation of Pathological Tremor
Author(s): F. Gianfelici
Pages: 1565-1574
6. Automated Induction of Heterogeneous Proximity Measures for Supervised Spectral Embedding
Author(s): E. Rodriguez-Martinez, T. Mu, J. Jiang, and J. Y. Goulermas
Pages: 1575-1587
7. Coordination of Multiagents Interacting Under Independent Position and Velocity Topologies
Author(s): J. Qin and C. Yu
Pages: 1588-1597
8. Learning Capability of Relaxed Greedy Algorithms
Author(s): S. Lin, Y. Rong, X. Sun, and Z. Xu
Pages: 1598-1608
9. Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection
Author(s): M. Tan, I. W. Tsang, and L. Wang
Pages: 1609-1622
10. Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting
Author(s): R. Coop, A. Mishtal, and I. Arel
Pages: 1623-1634
11. SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems
Author(s): X.-X. Zhang, Y. Jiang, H.-X. Li, and S.-Y. Li
Pages: 1635-1647
12. Single Image Super-Resolution With Multiscale Similarity Learning
Author(s): K. Zhang, X. Gao, D. Tao, and X. Li
Pages: 1648-1659
13. Tracking Algorithms for Multiagent Systems
Author(s): D. Meng, Y. Jia, J. Du, and F. Yu
Pages: 1660-1676
14. A Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps
Author(s): D. Wang and S. Tapan
Pages: 1677-1688
15. EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
Author(s): C.-T. Lin, S.-F. Tsai, and L.-W. Ko
Pages: 1689-1700
16. New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays
Author(s): G. Zhang and Y. Shen
Pages: 1701
Tuesday, October 1, 2013
Reminder: paper submission deadline for IEEE CIFEr 2014
A reminder that the deadline for submitting papers to the IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr) 2014 is November 1, 2013. This conference will be held in London, UK, 27-28 March, 2014.
Tuesday, September 24, 2013
On the importance of a good supervisor
One day, a fox was walking through the forest when he met a rabbit sitting outside a rabbit hole reading a pile of papers. "What are you doing?" the fox asked the rabbit. The rabbit looked up at the fox and replied "I'm doing the literature review for my thesis. It's on the superiority of rabbits over foxes. Would you like to come inside and discuss it?". The fox hungrily licked his lips, followed the rabbit into the rabbit hole, and was never seen again.
Some time later, a wolf was walking through the forest and saw the rabbit sitting outside of his rabbit hole making notes on a thick pile of paper with a big, red, pen. "What are you doing?" the wolf asked the rabbit. The rabbit looked up and replied "I'm revising my thesis". The wolf asked the rabbit "What's your thesis about?" and the rabbit said "It's on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The wolf hungrily licked his lips, followed the rabbit down the rabbit hole, and was never seen again.
Some time later, a hare was walking through the forest when he saw the rabbit sitting in the sun with a big, satisfied grin on this face. "Why are you looking so happy?" the hare asked the rabbit. The rabbit looked at the hare and said "I've just been awarded my PhD. My thesis was on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The hare, curious about such a topic, followed the rabbit down the rabbit hole into the warren. In one corner of the rabbit's room was a pile of fox bones. In another corner was a pile of wolf bones. Sitting between the two piles of bones was a lion.
So you see, it doesn't matter what your thesis is on, as long as your supervisor is a lion.
A newly published article (discussed in more detail by one of the authors here) has examined the influence of several factors that may determine how successful a scientist is in their career, where success is measured by the number of publications the scientist (biologists in this case) has. While factors such as gender and language had some slight effect, the factor that was most influential was the number of publications a scientist had before completing their PhD.
In other words, someone who has learned to produce papers before they finish their PhD is more likely to be able to continue producing papers after they have finished their PhD. To me this seems analagous to saying that someone who has learned how to drive can drive. Apparently, stating the blindingly obvious is original research as long as it uses statistics.
Who does a pre-PhD learn this paper-production skill from? Most of the time, from their supervisor. A supervisor who produces a lot of papers, and includes their students in the process of doing so, will produce PhD graduates who have the skills to produce papers post-PhD. If the supervisor doesn't teach the student how to publish, where else will they get this skill?
The most disturbing implication of this is that if a student chooses the wrong supervisor, they will have little chance of a successful career. The article linked to above states that the institution that the PhD graduates from has no influence on success and the influence of other factors is weak. As an aside, this reinforces something I've been saying for a while - that the reputation of an institution is good for marketing, but says little about the quality of the staff there.
The sentiment behind the story at the top of this post, is that as long as your supervisor is a good supervisor, you will be successful. This makes choosing the right supervisor probably the most critical decision an aspiring academic can ever make, yet they must make it when they have little knowledge and no experience on which to draw to make that decision. This is a huge problem - how many perfectly capable researchers have had their careers destroyed, before they have even begun, by a bad choice of supervisor? More importantly, how do those of us who are post-PhD stop it from happening in the future?
I really wish I had an answer to that question.
Some time later, a wolf was walking through the forest and saw the rabbit sitting outside of his rabbit hole making notes on a thick pile of paper with a big, red, pen. "What are you doing?" the wolf asked the rabbit. The rabbit looked up and replied "I'm revising my thesis". The wolf asked the rabbit "What's your thesis about?" and the rabbit said "It's on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The wolf hungrily licked his lips, followed the rabbit down the rabbit hole, and was never seen again.
Some time later, a hare was walking through the forest when he saw the rabbit sitting in the sun with a big, satisfied grin on this face. "Why are you looking so happy?" the hare asked the rabbit. The rabbit looked at the hare and said "I've just been awarded my PhD. My thesis was on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The hare, curious about such a topic, followed the rabbit down the rabbit hole into the warren. In one corner of the rabbit's room was a pile of fox bones. In another corner was a pile of wolf bones. Sitting between the two piles of bones was a lion.
So you see, it doesn't matter what your thesis is on, as long as your supervisor is a lion.
A newly published article (discussed in more detail by one of the authors here) has examined the influence of several factors that may determine how successful a scientist is in their career, where success is measured by the number of publications the scientist (biologists in this case) has. While factors such as gender and language had some slight effect, the factor that was most influential was the number of publications a scientist had before completing their PhD.
In other words, someone who has learned to produce papers before they finish their PhD is more likely to be able to continue producing papers after they have finished their PhD. To me this seems analagous to saying that someone who has learned how to drive can drive. Apparently, stating the blindingly obvious is original research as long as it uses statistics.
Who does a pre-PhD learn this paper-production skill from? Most of the time, from their supervisor. A supervisor who produces a lot of papers, and includes their students in the process of doing so, will produce PhD graduates who have the skills to produce papers post-PhD. If the supervisor doesn't teach the student how to publish, where else will they get this skill?
The most disturbing implication of this is that if a student chooses the wrong supervisor, they will have little chance of a successful career. The article linked to above states that the institution that the PhD graduates from has no influence on success and the influence of other factors is weak. As an aside, this reinforces something I've been saying for a while - that the reputation of an institution is good for marketing, but says little about the quality of the staff there.
The sentiment behind the story at the top of this post, is that as long as your supervisor is a good supervisor, you will be successful. This makes choosing the right supervisor probably the most critical decision an aspiring academic can ever make, yet they must make it when they have little knowledge and no experience on which to draw to make that decision. This is a huge problem - how many perfectly capable researchers have had their careers destroyed, before they have even begun, by a bad choice of supervisor? More importantly, how do those of us who are post-PhD stop it from happening in the future?
I really wish I had an answer to that question.
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