Wednesday, November 2, 2011
Reminder: paper deadline for KES-IIMSS 2012
A reminder that the deadline for submitting papers to the 5th International Conference on Intelligent Interactive Multimedia Systems and Services (KES IIMSS 2012) is 1st December 2011. This conference will be held in Gifu, Japan, 23-25 May 2012, simultaneously with the 4th International Conference on Intelligent Decision Technologies.
Tuesday, November 1, 2011
Reminder: paper submission deadline KES-IDT 2012
A reminder that the deadline for submitting papers to the 4th International Conference on Intelligent Decision Technologies (KES-IDT 2012) is 1 December 2011. This conference will be held in Gifu, Japan, 23-25 May, 2012.
Monday, October 31, 2011
Reminder: conference paper deadline ICFSNC 2012
A reminder that the deadline for papers submitted to the International Conference on Fuzzy Systems and Neural Computing (ICFSNC) 2012 is 30 November 2011. This conference will be held in Barcelona, Spain, April 11-13, 2012.
Friday, October 28, 2011
Conference paper submission deadline: BICS 2012
The deadline for submitting papers to the International Conference on Brain Inspired Cognitive Systems (BICS) 2012 is 15 January 2012. This conference will be held in Shenyang, China, 11-14 July, 2012.
Thursday, October 27, 2011
Reminder: paper submission deadline for ISNN 2012
A reminder that the deadline for submitting papers to the 2012 International Symposium on Neural Networks (ISNN 2012) is 15 January 2012. This symposium will be held in Shenyang, China, July 11-14, 2012.
Wednesday, October 26, 2011
Reminder: Paper submission deadline for ICNC-FSKD 2012
A reminder that the deadline for submitting papers to the 8th International Conference on Natural Computation and 9th International Conference on Knowledge Discovery is 15 November 2011. These conferences will be jointly held in Chongqing, China, 29-31 May, 2011.
Tuesday, October 25, 2011
Reminder: Paper deadline for IEEE CIBCB 2012
A reminder that the deadline for papers submitted to the 2012 conference on Computational Intelligence in Bioinformatics and Computational Biology is November 20, 2011. This conference will be held in San Diego, California, May 9-12, 2012.
Monday, October 24, 2011
Paper submission deadline: CBR-MD 2012
The deadline for submitting papers to the International Workshop Case-Based Reasoning (CBR-MD) 2012 is 13 April 2012. This workshop will be held in Berlin, Germany, 20 July 2012.
Friday, October 21, 2011
Call for papers: UCNC 2012
The deadline for submitting papers to the 11th Conference on Unconventional Computation and Natural Computation (UCNC) 2012 is 26 March 2012. This conference will be held in Orleans, France, 3-6 September, 2012.
Thursday, October 20, 2011
Call for papers: ESANN 2012
The deadline for submitting papers to the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2012 is 30 November 2011. This symposium will be held in Bruges, Belgium, 25-27 April, 2012.
Wednesday, October 19, 2011
Paper submission deadline: MLDM 2012
The deadline for submitting papers to the 8th Industrial Conference on Machine Learning and Data Mining (MLDM) 2012 is 18 December 2011. This conference will be held in Berlin, Germany, 16-20 July, 2012. MLDM will be held jointly with ICDM 2012.
Tuesday, October 18, 2011
Conference paper deadline: ICDM 2012
The deadline for papers submitted to the 12th Industrial Conference on Data Mining (ICDM) 2012 is 18 December 2011. This conference will be held in Berlin, Germany, 16-20 July, 2012.
Monday, October 17, 2011
Paper submission deadline: ICML 2012
The deadline for submitting papers to the International Conference on Machine Learning (ICML) 2012 is 24 February 2012. This conference will be held in Edinburgh, Scotland, June 26 - July 1, 2012.
Friday, October 14, 2011
On Presentations
Some presenters are applauded because the audience enjoyed their presentation. Other presenters are applauded because they ended their presentation.
Know which one you are.
Know which one you are.
Call for papers: WIVACE 2012
The deadline for submitting papers to the Italian Workshop on Artificial Life, Evolution and Complexity (WIVACE) 2012 is 6 January 2012. This workshop will be held in Parma, Italy, 20-21 February, 2012.
Thursday, October 13, 2011
Paper submission deadline: EvoStar 2012
The deadline for submitting papers to the European Conference on Evolutionary Computation (EvoStar) 2012 is 30 November 2012. This conference will be held in Malaga, Spain, 11-13 April, 2012.
Wednesday, October 12, 2011
Call for papers: ICCCI 2012
The deadline for papers submitted to the 4th International Conference on Computational Collective Intelligence (ICCCI) 2012 is 15 April 2012. This conference will be held in Ho Chi Minh City, Vietnam, 28-30 November, 2012.
Tuesday, October 11, 2011
Open research problems with Evolving Connectionist Systems
I described Evolving Connectionist Systems (ECoS) in an earlier post. A couple of years ago, I published a review article (PDF preprint) where I described the state of the art of ECoS, and identified several open research problems. There hasn't been much progress made in solving these problems, so I'm going to briefly describe them here, and hopefully stimulate a bit more work in this area. Of course, I'm doing a bit of work in some of these, but as I have a real job to do, I don't get as much time to spend on these problems as I'd like.
1) Input significance. With other ANN, especially the venerable MLP, it is possible to get an indication of how important each input variable is to the model. These methods are based on an analysis of the magnitude of the connection weights attached to each input neuron. This method won't work with ECoS networks, however, because the connection weights represent points in space. That is, the magnitude of the weight for an input neuron connection has nothing to do with how important that input is.
2) Optimisation of ECoS networks. While ECoS algorithms are fast learning, they can grow to be quite large, which makes them expensive in terms of memory and computational load. Ideally, it would be possible to reduce their size without sacrificing their accuracy. That is, it would be ideal if we could somehow eliminate redundant information in the ECoS and only retain that which is necessary for maintaining accuracy. I investigated a couple of methods of doing this in my PhD, and a few other people have looked at it as well, but no one has yet cracked the problem in terms of coming up with an optimisation algorithm that will significantly reduce the size of a trained ECoS network without significantly reducing its accuracy. Also, the most effective optimisation methods in the published work use evolutionary algorithms like genetic algorithms or evolution strategies. These are so computationally intensive that the speed advantages of ECoS are lost. An ECoS optimisation algorithm would ideally be as fast, or nearly as fast, as the ECoS training algorithm. It may be that this is inherently impossible.
3) Non-triangular fuzzy membership functions in EFuNN. The Evolving Fuzzy Neural Network EFuNN has triangular fuzzy membership functions (MF) embedded in its structure. These are fast and efficient, but other MF types (such as Gaussian) may be more useful for other applications.
4) Learning in the MF of EFuNN. The fuzzy MF in EFuNN are fixed, that is, they are set once and do not change during the life of the EFuNN. This is in contrast to the open, adaptive nature of EFuNN itself. An extension of the EFuNN learning algorithm that would allow the MF to adapt as the rest of the network adapts, would be extremely useful for data mining applications. This algorithm would have to be as fast as the rest of the EFuNN learning algorithm, which may rule out backpropagation training of the MF, as is used in other fuzzy system optimisation.
Although ECoS networks are very useful algorithms, they could be made even more useful if the problems above were solved. I'm working on some of them, but I would love to see others working on them as well. Contact me if you are interested in collaborating.
1) Input significance. With other ANN, especially the venerable MLP, it is possible to get an indication of how important each input variable is to the model. These methods are based on an analysis of the magnitude of the connection weights attached to each input neuron. This method won't work with ECoS networks, however, because the connection weights represent points in space. That is, the magnitude of the weight for an input neuron connection has nothing to do with how important that input is.
2) Optimisation of ECoS networks. While ECoS algorithms are fast learning, they can grow to be quite large, which makes them expensive in terms of memory and computational load. Ideally, it would be possible to reduce their size without sacrificing their accuracy. That is, it would be ideal if we could somehow eliminate redundant information in the ECoS and only retain that which is necessary for maintaining accuracy. I investigated a couple of methods of doing this in my PhD, and a few other people have looked at it as well, but no one has yet cracked the problem in terms of coming up with an optimisation algorithm that will significantly reduce the size of a trained ECoS network without significantly reducing its accuracy. Also, the most effective optimisation methods in the published work use evolutionary algorithms like genetic algorithms or evolution strategies. These are so computationally intensive that the speed advantages of ECoS are lost. An ECoS optimisation algorithm would ideally be as fast, or nearly as fast, as the ECoS training algorithm. It may be that this is inherently impossible.
3) Non-triangular fuzzy membership functions in EFuNN. The Evolving Fuzzy Neural Network EFuNN has triangular fuzzy membership functions (MF) embedded in its structure. These are fast and efficient, but other MF types (such as Gaussian) may be more useful for other applications.
4) Learning in the MF of EFuNN. The fuzzy MF in EFuNN are fixed, that is, they are set once and do not change during the life of the EFuNN. This is in contrast to the open, adaptive nature of EFuNN itself. An extension of the EFuNN learning algorithm that would allow the MF to adapt as the rest of the network adapts, would be extremely useful for data mining applications. This algorithm would have to be as fast as the rest of the EFuNN learning algorithm, which may rule out backpropagation training of the MF, as is used in other fuzzy system optimisation.
Although ECoS networks are very useful algorithms, they could be made even more useful if the problems above were solved. I'm working on some of them, but I would love to see others working on them as well. Contact me if you are interested in collaborating.
Monday, October 10, 2011
Call for papers: IEEE SSCI 2013
The deadline for the IEEE Symposium Series in Computational Intelligence 2013 is 10 October 2012. This series of symposia will be held in Singapore 16-19 April 2013.
Thursday, October 6, 2011
The problem with academic journals 2
In this earlier post I linked to an article by George Monbiot that discussed the biggest problem with academic journals, which is the enormous expense of accessing journals or journal articles, given that the content and quality control are all provided for free.
Monbiot's original article touched off something of a storm in the academic web, with some echoing his sentiments, and others (some of whom, at least, were connected to the publishers) attacking him. The publishers themselves tried to justify their charges by writing a lot without saying very much (I find as I lurch remorselessly towards 40 that I have less and less tolerance for corporate weasel-wording). Still others discussed whether open access journals are feasible alternatives.
I serve on the editorial board of an open access journal, so I know that they can have a problem with legitimacy, or at least with being taken seriously. But open access journals are still fairly new, and it takes time for a journal to build up its article base, which in turn allows it to build up its citation rate, which in turn builds its impact factor, which is the most important, or at least the most widely known, measure of a journal's success, despite the problems with it.
From my own point of view, I review papers for open access journals with the same care as I review any other paper. As a working scientist and active author, if I can't find a copy of an article online, I won't cite it. It's the authors of the paper who are missing out then, it makes no difference to me whatsoever if they get cited or not.
It's interesting that an august institution like Princeton university, in an effort to promote open-access journals, has recently enacted a policy that forbids its staff from assigning copyright of articles to journal publishers. Let's hope that we see more top institutions doing the same thing: the sooner we break the stranglehold on top science held by the old publishers, the better for everyone.
Monbiot's original article touched off something of a storm in the academic web, with some echoing his sentiments, and others (some of whom, at least, were connected to the publishers) attacking him. The publishers themselves tried to justify their charges by writing a lot without saying very much (I find as I lurch remorselessly towards 40 that I have less and less tolerance for corporate weasel-wording). Still others discussed whether open access journals are feasible alternatives.
I serve on the editorial board of an open access journal, so I know that they can have a problem with legitimacy, or at least with being taken seriously. But open access journals are still fairly new, and it takes time for a journal to build up its article base, which in turn allows it to build up its citation rate, which in turn builds its impact factor, which is the most important, or at least the most widely known, measure of a journal's success, despite the problems with it.
From my own point of view, I review papers for open access journals with the same care as I review any other paper. As a working scientist and active author, if I can't find a copy of an article online, I won't cite it. It's the authors of the paper who are missing out then, it makes no difference to me whatsoever if they get cited or not.
It's interesting that an august institution like Princeton university, in an effort to promote open-access journals, has recently enacted a policy that forbids its staff from assigning copyright of articles to journal publishers. Let's hope that we see more top institutions doing the same thing: the sooner we break the stranglehold on top science held by the old publishers, the better for everyone.
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