Reminder: paper submission deadline for ISNN 2015

A reminder that the paper submission deadline for the 12th International Symposium on Neural Networks (ISNN) 2015 is May 15, 2015. This conference will be held in Jeju, Korea, 15-18 October, 2015.

IEEE Transactions on Neural Networks and Learning Systems: Volume 26, Issue 5, May 2015

1. Self-Organizing Neural Networks Integrating Domain Knowledge and Reinforcement Learning
Author(s): Teck-Hou Teng; Ah-Hwee Tan; Jacek M. Zurada
Page(s): 889 - 902

2. Variable Neural Adaptive Robust Control: A Switched System Approach
Author(s): Jianming Lian; Jianghai Hu; Stanislaw H. Zak
Page(s): 903 - 915

3. Integral Reinforcement Learning for Continuous-Time Input-Affine Nonlinear Systems With Simultaneous Invariant Explorations
Author(s): Jae Young Lee; Jin Bae Park; Yoon Ho Choi
Page(s): 916 - 932

4. Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
Author(s): Shing Chiang Tan; Junzo Watada; Zuwairie Ibrahim; Marzuki Khalid
Page(s): 933 - 950

5. Graph Embedded Nonparametric Mutual Information for Supervised Dimensionality Reduction
Author(s): Dimitrios Bouzas; Nikolaos Arvanitopoulos; Anastasios Tefas
Page(s): 951 - 963

6. The Minimum Risk Principle That Underlies the Criteria of Bounded Component Analysis
Author(s): Sergio Cruces; Ivan Duran-Diaz
Page(s): 964 - 981

7. A One-Class Kernel Fisher Criterion for Outlier Detection
Author(s): Franck Dufrenois
Page(s): 982 - 994

8. Semi-Supervised Nearest Mean Classification Through a Constrained Log-Likelihood
Author(s): Marco Loog; Are Charles Jensen
Page(s): 995 - 1006

9. Adaptive NN Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems
Author(s): Yan-Jun Liu; Li Tang; Shaocheng Tong; C. L. Philip Chen
Page(s): 1007 - 1018

10. Transfer Learning for Visual Categorization: A Survey
Author(s): Ling Shao; Fan Zhu; Xuelong Li
Page(s): 1019 - 1034

11. Robust Sensorimotor Representation to Physical Interaction Changes in Humanoid Motion Learning
Author(s): Toshihiko Shimizu; Ryo Saegusa; Shuhei Ikemoto; Hiroshi Ishiguro; Giorgio Metta
Page(s): 1035 - 1047

12. A New Method for Data Stream Mining Based on the Misclassification Error
Author(s): Leszek Rutkowski; Maciej Jaworski; Lena Pietruczuk; Piotr Duda
Page(s): 1048 - 1059

13. Learning to Track Multiple Targets
Author(s): Xiao Liu; Dacheng Tao; Mingli Song; Luming Zhang; Jiajun Bu; Chun Chen
Page(s): 1060 - 1073

14. Adaptive Neural Control of Nonlinear MIMO Systems With Time-Varying Output Constraints
Author(s): Wenchao Meng; Qinmin Yang; Youxian Sun
Page(s): 1074 - 1085

15. Very Sparse LSSVM Reductions for Large-Scale Data
Author(s): Raghvendra Mall; Johan A. K. Suykens
Page(s): 1086 - 1097

16. Sparse Multivariate Gaussian Mixture Regression
Author(s): Luis Weruaga; Javier Via
Page(s): 1098 - 1108

17. Variational Inference With ARD Prior for NIRS Diffuse Optical Tomography
Author(s): Atsushi Miyamoto; Kazuho Watanabe; Kazushi Ikeda; Masa-Aki Sato
Page(s): 1109 - 1114

18. An Efficient Topological Distance-Based Tree Kernel
Author(s): Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti
Page(s): 1115 - 1120

Neural Networks Volume 66, Pages 1-138, June 2015

1) Event-triggered asynchronous intermittent communication strategy for synchronization in complex dynamical networks
Pages: 1-10
Author(s): Huaqing Li, Xiaofeng Liao, Guo Chen, David J. Hill, Zhaoyang Dong, Tingwen Huang

2) Multilingual part-of-speech tagging with weightless neural networks 
Pages: 11-21
Author(s): Hugo C.C. Carneiro, Felipe M.G. Franca, Priscila M.V. Lima

3) Hierarchical neural networks perform both serial and parallel processing
Pages: 22-35
Author(s): Elena Agliari, Adriano Barra, Andrea Galluzzi, Francesco Guerra, Daniele Tantari, Flavia Tavani

4) Diversifying customer review rankings
Pages: 36-45
Author(s): Ralf Krestel, Nima Dokoohaki

5) Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays  
Pages: 46-63
Author(s): R. Rakkiyappan, N. Sakthivel, Jinde Cao

6) Multi-frame image super resolution based on sparse coding  
Pages: 64-78
Author(s): Toshiyuki Kato, Hideitsu Hino, Noboru Murata

7) A digital implementation of neuron–astrocyte interaction for neuromorphic applications 
Pages: 79-90
Author(s): Soheila Nazari, Karim Faez, Mahmood Amiri, Ehsan Karami

8) Asynchronous event-based corner detection and matching  
Pages: 91-106
Author(s): Xavier Clady, Sio-Hoi Ieng, Ryad Benosman

9) Phase synchronization of coupled bursting neurons and the generalized Kuramoto model  
Pages: 107-118
Author(s): F.A.S. Ferrari, R.L. Viana, S.R. Lopes, R. Stoop

10) Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays  
Pages: 119-130
Author(s): Jin Hu, Jun Wang

11) A new delay-independent condition for global robust stability of neural networks with time delays
Pages: 131-137
Author(s): Ruya Samli

Reminder: paper submission deadline for ICSI3 2015

A reminder that the deadline for submitting papers to the International Congress on Systems Immunology, Immunoinformatics & Immune-Computation (ICSI3) 2015 is 28 April, 2015. This conference will be held in Taormina, Italy, 17-18 July, 2015.

Paper submission deadline: ISNN 2015

The paper submission deadline for the 12th International Symposium on Neural Networks (ISNN) 2015 is May 15, 2015. This conference will be held in Jeju, Korea, 15-18 October, 2015.

Conference paper deadline: TAAI 2015

The deadline for submitting papers to the 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2015) is July 12, 2015. This conference will be held in Tainan, Taiwan, November 20-22, 2015.

Conference paper deadline: BESC 2015

The paper submission deadline for Behavioral, Economic and Socio-Cultural Computing (BESC) 2015 is 17 June, 2015. This conference will be held in Nanjing, China, 30 October - 1 November, 2015.

Paper submission deadline: IEEE SSCI 2015

The deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (IEEE SSCI) 2015 is June 14, 2015. This conference will be held in Cape Town, South Africa, December 7-10, 2015.

Conference submission deadline SMAP 2015

The paper submission deadline for the 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) 2015 is June 12, 2015. This conference will be held in  Trento, Italy, November 5-6, 2015.

Conference paper deadline: DSAA'15

The paper submission deadline for the IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2015 is 18 May, 2015. This conference will be held in Paris, France, 19-21 October, 2015.

Conference paper deadline: ICSI3 2015

The deadline for submitting papers to the International Congress on Systems Immunology, Immunoinformatics & Immune-Computation (ICSI3) 2015 is 28 April, 2015. This conference will be held in Taormina, Italy, 17-18 July, 2015.

Conference paper deadline: ICICIP 2015

The deadline for submitting paper to the Sixth International Conference on Intelligent Control and Information Processing (ICICIP) 2015 is June 15, 2015. This conference will be held in Wuhan, China, November 26-28, 2015.

IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 4, April 2015

1. Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach
Authors: Yi-Fei Pu; Ji-Liu Zhou; Yi Zhang; Ni Zhang; Guo Huang; Patrick Siarry
Page(s): 653 - 662

2. Quaternion-Valued Echo State Networks
Authors: Yili Xia; Cyrus Jahanchahi; Danilo P. Mandic
Page(s): 663 - 673

3. Successive Overrelaxation for Laplacian Support Vector Machine
Authors: Zhiquan Qi; Yingjie Tian; Yong Shi
Page(s): 674 - 683

4. Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming
Authors: Biao Luo; Huai-Ning Wu; Han-Xiong Li
Page(s): 684 - 696

5. Convolutive Bounded Component Analysis Algorithms for Independent and Dependent Source Separation
Authors: Huseyin A. Inan; Alper T. Erdogan
Page(s): 697 - 708

6. Gaussian Kernel Width Optimization for Sparse Bayesian Learning
Authors: Yalda Mohsenzadeh; Hamid Sheikhzadeh
Page(s): 709 - 719

7. Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering
Authors: Siamak Mehrkanoon; Carlos Alzate; Raghvendra Mall; Rocco Langone; Johan A. K. Suykens
Page(s): 720 - 733

8. Impulsive Stabilization and Impulsive Synchronization of Discrete-Time Delayed Neural Networks
Authors: Wu-Hua Chen; Xiaomei Lu; Wei Xing Zheng
Page(s): 734 - 748

9. A Universal Concept Based on Cellular Neural Networks for Ultrafast and Flexible Solving of Differential Equations
Authors: Jean Chamberlain Chedjou; Kyandoghere Kyamakya
Page(s): 749 - 762

10. On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures
Authors: Eric Aislan Antonelo; Benjamin Schrauwen
Page(s): 763 - 780

11. Coupled Attribute Similarity Learning on Categorical Data
Authors: Can Wang; Xiangjun Dong; Fei Zhou; Longbing Cao; Chi-Hung Chi
Page(s): 781 - 797

12. Topology-Based Clustering Using Polar Self-Organizing Map
Authors: Lu Xu; Tommy W. S. Chow; Eden W. M. Ma
Page(s): 798 - 808

13. Robust Consensus Tracking Control for Multiagent Systems With Initial State Shifts, Disturbances, and Switching Topologies
Authors: Deyuan Meng; Yingmin Jia; Junping Du
Page(s): 809 - 824

14. $L_{1}$ -Norm Low-Rank Matrix Factorization by Variational Bayesian Method
Authors: Qian Zhao; Deyu Meng; Zongben Xu; Wangmeng Zuo; Yan Yan
Page(s): 825 - 839

15. Nonlinear Model Predictive Control Based on Collective Neurodynamic Optimization
Authors: Zheng Yan; Jun Wang
Page(s): 840 - 850

16. Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data
Authors: Ruizhuo Song; Frank Lewis; Qinglai Wei; Hua-Guang Zhang; Zhong-Ping Jiang; Dan Levine
Page(s): 851 - 865

17. Infinite Horizon Self-Learning Optimal Control of Nonaffine Discrete-Time Nonlinear Systems
Authors: Qinglai Wei; Derong Liu; Xiong Yang
Page(s): 866 - 879

18. A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance
Authors: Gregory Ditzler; Robi Polikar; Gail Rosen
Page(s): 880 - 886

IEEE Transactions on Autonomous Mental Development, Volume 7, Number 1, March 2015

1. Editorial IEEE Transactions on Autonomous Mental Development
Author(s): Cangelosi, A
Page(s): 1 - 2

2. Ecological Active Vision: Four Bioinspired Principles to Integrate Bottom–Up and Adaptive Top–Down Attention Tested With a Simple Camera-Arm Robot
Author(s): Ognibene, D. ; Baldassare, G.
Page(s): 3 - 25

3. A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor Control
Author(s): Shim, V.A. ; Ranjit, C.S.N. ; Bo Tian ; Miaolong Yuan ; Huajin Tang
Page(s): 26 - 38

4. Mental States, EEG Manifestations, and Mentally Emulated Digital Circuits for Brain-Robot Interaction
Author(s): Bozinovski, S. ; Bozinovski, A.
Page(s): 39 - 51

5. Can Real-Time, Adaptive Human–Robot Motor Coordination Improve Humans’ Overall Perception of a Robot?
Author(s): Qiming Shen ; Dautenhahn, K. ; Saunders, J. ; Kose, H.
Page(s): 52 - 64

IEEE Transactions on Evolutionary Computation, Volume 19, Number 2, April 2015

1. Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming
Author(s): Ni, J. ; Rockett, P.
Page(s): 157 - 166

2. Parameter Control in Evolutionary Algorithms: Trends and Challenges
Author(s): Karafotias, G. ; Hoogendoorn, M. ; Eiben, A.E.
Page(s): 167 - 187

3. Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance
Author(s): Wang, P. ; Emmerich, M. ; Li, R. ; Tang, K. ; Back, T. ; Yao, X.
Page(s): 188 - 200

4. An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
Author(s): Zhang, X. ; Tian, Y. ; Cheng, R. ; Jin, Y.
Page(s): 201 - 213

5. Probabilistic Dominance in Multiobjective Reliability-Based Optimization: Theory and Implementation
Author(s): Filomeno Coelho, R.
Page(s): 214 - 224

6. Visualization of Pareto Front Approximations in Evolutionary Multiobjective Optimization: A Critical Review and the Prosection Method
Author(s): Tusar, T. ; Filipic, B.
Page(s): 225 - 245

7. Inducing Niching Behavior in Differential Evolution Through Local Information Sharing
Author(s): Biswas, S. ; Kundu, S. ; Das, S.
Page(s): 246 - 263

8. Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems
Author(s): Ishibuchi, H. ; Akedo, N. ; Nojima, Y.
Page(s): 264 - 283

9. Toward the Coevolution of Novel Vertical-Axis Wind Turbines
Author(s): Preen, R.J. ; Bull, L.
Page(s): 284 - 294

10. On the Easiest and Hardest Fitness Functions
Author(s): He, J. ; Chen, T. ; Yao, X.
Page(s): 295 - 305

IEEE Transactions on Computational Intelligence and AI in Games, Volume 7, Number 1, March 2015

Editorial
1. Editorial: IEEE Transactions on Computational Intelligence and AI in Games
Page(s): 1 - 2

Regular Papers
2. On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing
Author(s): Yang Gao ; Yan Chen ; Liu, K.J.R.
Page(s): 3 - 15

3. An Enhanced Solver for the Game of Amazons
Author(s): Jiaxing Song ; Muller, M.
Page(s): 16 - 27

4. Job-Level Alpha-Beta Search
Author(s): Jr-Chang Chen ; I-Chen Wu ; Wen-Jie Tseng ; Bo-Han Lin ; Chia-Hui Chang
Page(s): 28 - 38

5. Suspenser: A Story Generation System for Suspense
Author(s): Yun-Gyung Cheong ; Young, R.M.
Page(s): 39 - 52

6. Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents
Author(s): Ashlock, D. ; Brown, J.A. ; Hingston, P.
Page(s): 53 - 65

7. Design and Implementation of Chinese Dark Chess Programs
Author(s): Shi-Jim Yen ; Cheng-Wei Chou ; Jr-Chang Chen ; I-Chen Wu ; Kuo-Yuan Kao
Page(s): 66 - 74

8. Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning
Author(s): Ersen, M. ; Sariel, S.
Page(s): 75 - 87

9. Learning-Based Procedural Content Generation
Author(s): Roberts, J. ; Ke Chen
Page(s): 88 - 101

Short Papers
10. Sequential Halving Applied to Trees
Author(s): Cazenave, T.
Page(s): 102 - 105

Neural Networks, Volume 65 , Pages 1-126, May 2015

1. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning  
Pages: 1-17
Author(s): Yong Peng, Bao-Liang Lu, Suhang Wang

2. Computational cognitive models of spatial memory in navigation space: A review 
Pages: 18-43
Author(s): Tamas Madl, Ke Chen, Daniela Montaldi, Robert Trappl

3. Attention modeled as information in learning multisensory integration  
Pages: 44-52
Author(s): Johannes Bauer, Sven Magg, Stefan Wermter

4. A new class of multi-stable neural networks: Stability analysis and learning process  
Pages: 53-64
Author(s): E. Bavafaye Haghighi, G. Palm, M. Rahmati, M.J. Yazdanpanah

5. Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays  
Pages: 65-79
Author(s): Xiaobing Nie, Wei Xing Zheng

6. Pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control  
Pages: 80-91
Author(s): Xinsong Yang, Jinde Cao, Jianlong Qiu

7. Robust L1-norm two-dimensional linear discriminant analysis  
Pages: 92-104
Author(s): Chun-Na Li, Yuan-Hai Shao, Nai-Yang Deng

8. New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations  
Pages: 105-114
Author(s): Zuowei Cai, Lihong Huang, Lingling Zhang

9. Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples 
Pages: 115-125
Author(s): Luca Oneto, Alessandro Ghio, Sandro Ridella, Davide Anguita

Measurement Theory

Just because something is expressed as a number, doesn't mean you can do arithmetic with it. Let's say I give you three numbers: 1,2,3. What's the mean of these numbers? (1+2+3)/3=2, right? Well, what if I told you that 1=apple, 2=pear and 3=banana? What's the mean of an apple, a pear and a banana? Plainly, the question is ridiculous. Yet there is still a substantial number of people in science, and in computational intelligence, who fall into this trap, especially when presenting data to models like neural networks.

Neural networks aren't magic: they can't tell what a number submitted to their input layers mean, they just multiply them by their connection weights, sum the products and apply a transformation function to them. So if the numbers you are submitting to them represent classes, rather than measurements, what are they really modelling?

Measurement is the most fundamental part of data collection, as all natural data originates as measurements of properties of events. Measurements should represent reality and relationships between measurements should reflect the relationships between attributes. This is most important is consideration is given to the principle that data represents reality: as the source of data, measurements must yield and adequate representation of reality.

Measurement theory, as originated by Stevens, helps us achieve this. By specifying and formalising what exactly measurement is, we can better use measurement to gather data. By understanding exactly what the numbers mean, we can better analyse and transform the data into information and knowledge, while avoiding such traps as making meaningless statements about the numbers or performing a meaningless transformation on the data. A crucial point to bear to in mind is that measurements represent reality but are not the same as reality.

Measurement Scales

At the heart of Steven’s measurement theory is the concept of measurement scales. Four such scales are defined (although other have been added since) where each scale is distinguished according to four characteristics:

• Distinctiveness: individuals are assigned different values if the property being measure is different.
• Ordering in magnitude: larger numbers represent greater quantities of the property being measured;
• Equal intervals: a difference in measurement represents the same difference in the property.
• Absolute zero: a measurement of zero represents an absence of the property being measured.

These four characteristics define the “strength” of the measurement scale. The scales, from “weakest” to “strongest” are:

• Nominal
• Ordinal
• Interval
• Ratio
• Absolute

Also associated with each of the measurement scales are specific, permissible statistics and transformations. The term permissible is slightly misleading: if a statistic is not permissible for a certain scale, it is not forbidden. Rather, the results of that statistic or transformation are not reliable, with the unreliability of the result determined by the way in which the measurements were made. Permissible statistics and transformations are simply those statistics and transformations that yield reliable results. A permissible statistic tells us something meaningful about the data, while a permissible transformation maintains the properties of the data as appropriate for the particular scale. A statistic may still be applied to data from a scale, for which that statistic is impermissible, and it may yield useful results, but these results need to be treated with caution, and interpreted within the context of the original measurements. Note also that permissible statistics and transformations are cumulative across scales, that is, all statistics and transformation permissible for a lower scale are permissible for a higher scale.

Nominal Scale

The nominal scale is the weakest of the measurement scales. It possesses only the characteristic of distinctiveness. In other words, if the same attribute of two individuals are assigned the same number, then the attributes are identical. No other conclusions may be drawn from those numbers, as they are simply arbitrary numeric labels. For example, the colours Red, Green, and Blue can be placed on the nominal scale with the measurements Red=1, Green=2, Blue=3. However, two reds do not make a green. They could just as easily be labelled Green=1, Blue=2, Red=3, or any other permutation, without altering their distinctiveness. The only permissible statistics for nominal scale measurements are the number of cases and the mode. Permissible transformations are permutations and one-to-one substitutions.

Ordinal Scale

Measurements on the ordinal scale have the properties of distinctiveness and ordering in magnitude. In other words, objects are ordered in the scale according to some pair-wise comparison. That is, measurements on this scale can be compared to one another with the equality, greater than or less than operators. However, while we can say that one measurement is greater than or less than another, we cannot say how different they are. Numbers in this scale are categories; they do not have the arithmetic properties of numbers. An example of an ordinal scale measurement is teaching evaluations: a teacher’s performance is evaluated by students over several variables, with the performance being rated from one to five, with one being “Poor” and five being “Excellent”. While it is meaningful to draw the conclusion that a score of four is better than a score of two, it is not meaningful to draw the conclusion that a score of four is twice as good as a score of two, nor is it meaningful to say that a score of five is the same “distance” from a score of three, as a score of three is from one. Permissible statistics introduced at the ordinal scale are medians and percentiles. Permissible transformations introduced are monotonic increasing functions, that is, any transformation that will maintain the order of the individuals.

Interval Scale

Measurements on the interval scale have the characteristics of distinctiveness, ordering in magnitude and equal intervals. In this scale, objects are placed in order on a number line with an arbitrary zero point and an arbitrary interval between objects. While the numerical values have no significance other than as labels, differences between the values do have meaning. An example of an interval scale is the date in years. The common era (CE) scale has an arbitrary zero point (set at the putative time of the birth of Christ) and equally sized intervals (the length of a year does not vary, excepting leap years, which actually make up for errors caused by the slight mismatch between the arbitrary length of the year set at 365 days and the actual length of the Earth’s orbit).  It is meaningful to say that 1973 is later than 1928, and that the difference between 1999 and 1973 is twice the difference between 1986 and 1973. It is not meaningful, however, to say that 2004 is twice the year that 1002 was. Permissible statistics introduced at the interval scale are the mean, standard deviation, rank-order correlation and product-moment correlation. Permissible transformations introduced are linear transformations of the format y=ax+b, where x is the measurement, and the constant a cannot be zero. In other words, permissible transformations are those transformations that preserve the order of the objects, and the relative intervals between them.

Ratio Scale

Measurements on the ratio scale have the characteristics of distinctiveness, ordering in magnitude, equal intervals and absolute zero. In this scale, objects are placed in order on a number line with equally sized intervals and a true zero point. A measurement of zero on the ratio scale indicates the absence of the property being measured. A ratio scale can also be defined as the differences between two interval measures: a difference of zero between two interval measurements indicates an absence of difference. In the ratio scale, the values themselves have significance, as do the differences and ratios of those values. Many properties in physics are ratio scale measurements. An example of this is speed. An object with a speed of zero isn’t moving, that is, it has no speed, while an object moving at fifty metres per second is twice as fast as an object moving at twenty-five metres per second.

Permissible statistics introduced at the ratio scale are the coefficient of variation, and permissible transformations are affine transformations, that is, y=ax.

Absolute Scale

Whereas measurements on the ratio scale have an absolute zero point, measurements on the absolute scale have and absolute zero and an absolute upper bound. The classical example of this is probabilities: the probability of an event can range from zero (the event will never happen) to one (the event will always happen). A probability of less than zero or greater than one is meaningless.

Only affine transformations are permissible for measurements on the absolute scale.

Transforming Between Scales

It is possible to transform a measurement made on a particular measurement scale to a weaker scale only. This transformation will involve a loss of information, and cannot be reversed. In other words, it is not possible to transform to a higher measurement scale. For example, consider the heights, in metres, of a group of three people. One person is 1.4 metres tall, the second is 1.8 metres tall, and the third is 2 metres tall. If we say that a person’s height is 1 if they are short, 2 if they are average and 3 if they are tall, then it is possible to transform these ratio scale measurements into the ordinal scale, by assigning the first person’s height a value of 1, the second a value of 2 and the third a value of 3. However, if we know only that a persons height is 2 on this scale, we cannot determine exactly what their true height is.

Summary

The major implication of this is that data must be collected with great care. Once a measurement is made on a particular measurement scale, it cannot be transformed into a higher scale. Once the measurement is made, no further information can be associated with it.

You must know which scale the measurements belong to, as they will determine what you can meaningfully do with the data. They will also inform as to how you represent the data for presentation to your models. A working knowledge of measurement theory, therefore, is essential for any serious practitioner in computational intelligence.

IEEE Transactions on Neural Networks and Learning Systems: Volume 26, Issue 3, March 2015

1. An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification
Authors: Mohammed Falah Mohammed; Chee Peng Lim
Page(s): 417 - 429

2. ML-TREE: A Tree-Structure-Based Approach to Multilabel Learning
Authors: Qingyao Wu; Yunming Ye; Haijun Zhang; Tommy W. S. Chow; Shen-Shyang Ho
Page(s): 430 - 443

3. Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Authors: Kai Zhang; Liang Lan; James T. Kwok; Slobodan Vucetic; Bahram Parvin
Page(s): 444 - 457

4. Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering
Authors: Hansenclever F. Bassani; Aluizio F. R. Araujo
Page(s): 458 - 471

5. Neural Network-Based Finite Horizon Stochastic Optimal Control Design for Nonlinear Networked Control Systems
Authors: Hao Xu; Sarangapani Jagannathan
Page(s): 472 - 485

6. Neural Network-Based Finite-Horizon Optimal Control of Uncertain Affine Nonlinear Discrete-Time Systems
Authors: Qiming Zhao; Hao Xu; Sarangapani Jagannathan
Page(s): 486 - 499

7. Maintaining the Integrity of Sources in Complex Learning Systems: Intraference and the Correlation Preserving Transform
Authors: Clive Cheong Took; Scott C. Douglas; Danilo P. Mandic
Page(s): 500 - 509

8. Exponential Synchronization of Complex Networks of Linear Systems and Nonlinear Oscillators: A Unified Analysis
Authors: Jiahu Qin; Huijun Gao; Wei Xing Zheng
Page(s): 510 - 521

9. Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes
Authors: Harold Soh; Yiannis Demiris
Page(s): 522 - 536

10. Bayesian Nonparametric Adaptive Control Using Gaussian Processes
Authors: Girish Chowdhary; Hassan A. Kingravi; Jonathan P. How; Patricio A. Vela
Page(s): 537 - 550

11. Generalization Performance of Radial Basis Function Networks
Authors: Yunwen Lei; Lixin Ding; Wensheng Zhang
Page(s): 551 - 564

12. Second-Order Global Consensus in Multiagent Networks With Random Directional Link Failure
Authors: Huaqing Li; Xiaofeng Liao; Tingwen Huang; Wei Zhu; Yanbing Liu
Page(s): 565 - 575

13. Novelty Detection Using Level Set Methods
Authors: Xuemei Ding; Yuhua Li; Ammar Belatreche; Liam P. Maguire
Page(s): 576 - 588

14. A Simplified Adaptive Neural Network Prescribed Performance Controller for Uncertain MIMO Feedback Linearizable Systems
Authors: Achilles Theodorakopoulos; George A. Rovithakis
Page(s): 589 - 600

15. Convergence Analysis of the FOCUSS Algorithm
Authors: Kan Xie; Zhaoshui He; Andrzej Cichocki
Page(s): 601 - 613

16. GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming
Authors: Zhen Ni; Haibo He; Dongbin Zhao; Xin Xu; Danil V. Prokhorov
Page(s): 614 - 627

17. The Generalization Ability of Online SVM Classification Based on Markov Sampling
Authors: Jie Xu; Yuan Yan Tang; Bin Zou; Zongben Xu; Luoqing Li; Yang Lu
Page(s): 628 - 639

18. Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors
Authors: Jinpeng Yu; Peng Shi; Wenjie Dong; Bing Chen; Chong Lin
Page(s): 640 - 645

19. A Unified Approach to Universal Prediction: Generalized Upper and Lower Bounds
Authors: Nuri Denizcan Vanli; Suleyman S. Kozat
Page(s): 646 - 651

Reminder: conference paper deadline for IEEE CIG 2015

A reminder that the paper submission deadline for the 2015 IEEE Conference on Computational Intelligence in Games (IEEE CIG) is April 2, 2015. This conference will be held in Tainan, Taiwan, 31 August to 2 September, 2015.