I just got an email from Martin Butz about a discussion session being planned for IWLCS 2010 and his request to pass it along. Hope all is well and you are going to attend GECCO this year. Regardless if you…
I just got an email from Martin Butz about a discussion session being planned for IWLCS 2010 and his request to pass it along.
Hope all is well and you are going to attend GECCO this year.
Regardless if you attend or not:
Jaume asked me to lead a discussion session on
“LCS representations, operators, and scalability – what is next?”
… or similar during IWLCS… Basically everything besides datamining, because there will be another session on that topic.
So, I am sure you all have some issues in mind that you think should be tackled / addressed / discussed at the workshop and in the near future.
Thus, I would be very happy to receive a few suggestions from your side – anything is welcome – I will then compile the points raised in a few slides to try and get the discussion going at the workshop.
Thank you for any feedback you can provide.
Looking forward to seeing you soon!
Martin
P.S.: Please feel free to also forward this message or tell me, if you think this Email should be still sent to other people…
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“On the Road to Competence” is a slide deck by Jurgen Appelo with interesting analogies between learning classifier systems and software development. Definitely worth taking a look at it. Related posts:NIGEL 2006 Part II: Dasgupta vs. Booker Large Scale Data Mining using Genetics-Based Machine Learning Software for fast rule matching using vector instructions
Below you may find the slides of the GECCO 2009 tutorial that Jaume Bacardit and I put together. Hope you enjoy it.
Slides
Abstract
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes […]
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.
This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.
After coming back from GECCO I just uploaded the last of the NIGEL 2006 talks at LCS & GBML Central. This last talk was by Jaume Bacardit and GBML for protein structure prediction.
Related posts:NIGEL 2006 Part V: Bernardó vs. LanziNIGEL 2006 Part IV: Llorà vs. CasillasNIGEL 2006 Part III: Butz vs. Barry
After coming back from GECCO I just uploaded the last of the NIGEL 2006 talks at LCS & GBML Central. This last talk was by Jaume Bacardit and GBML for protein structure prediction.
After the vacation break, two more NIGEL 2006 talks are available at LCS & GBML Central. This week Ester Bernardó presents how LCS can perform in the presence of class imbalance, whereas Lanzi continues his quest on computed predictions.
Related posts:NIGEL 2006 Part IV: Llorà vs. CasillasTranscoding NIGEL 2006 videosNIGEL 2006 Part III: Butz […]
After the vacation break, two more NIGEL 2006 talks are available at LCS & GBML Central. This week Ester Bernardó presents how LCS can perform in the presence of class imbalance, whereas Lanzi continues his quest on computed predictions.
Two more NIGEL 2006 talks are available at LCS & GBML Central. This week Xavier Llorà presents how linkage learning can be achieve in Pittsburgh LCS, whereas Jorge Casillas reviews his work using XCS and Fuzzy LCS.
Related posts:NIGEL 2006 Part III: Butz vs. BarryNIGEL 2006 Part II: Dasgupta vs. BookerNIGEL 2006 Part V: […]
Two more NIGEL 2006 talks are available at LCS & GBML Central. This week Xavier Llorà presents how linkage learning can be achieve in Pittsburgh LCS, whereas Jorge Casillas reviews his work using XCS and Fuzzy LCS.
NIGEL 2006 talks is available at LCS & GBML Central. This week Martin Butz review reviews the state of the union of XCS, where as Alwyn Barry introduces the theoretical framework for LCS that he and Jan Drugowitsch worked on.
Related posts:NIGEL 2006 Part IV: Llorà vs. CasillasNIGEL 2006 Part II: Dasgupta vs. BookerNIGEL […]
NIGEL 2006 talks is available at LCS & GBML Central. This week Martin Butz review reviews the state of the union of XCS, where as Alwyn Barry introduces the theoretical framework for LCS that he and Jan Drugowitsch worked on.