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Archive for the ‘Research’ Category

Last call for participation to the Lanscape Contest

Icon Written by admin on April 18, 2010 – 9:16 am

The landscape contest is a research competition aimed at finding out the relation between data complexity and the performance of learners. Comparing your techniques to those of other participants may contribute to enrich our understanding of the behavior of machine learning techniques and open further research lines.
The contest will take place [...]



Facetwise analysis of XCS for problems with class imbalances

Icon Written by admin on September 19, 2009 – 4:51 pm

by Albert Orriols-Puig, Ester Bernadó-Mansilla, David E. Goldberg, Kumara Sastry, and Pier Luca Lanzi. IEEE Transactions on Evolutionary Computation, doi=10.1109/ TEVC.2009.2019829, [Publisher site].



Save analysis of your results

Icon Written by admin on June 28, 2009 – 12:46 pm

Over the last few years, the increasing interest in machine learning has resulted in the design and development of several competitive learners. Usually, the performance of these methods is evaluated by comparing the new techniques to state-of-the-art methods over a collection of real-world problems.
In early days, these comparisons followed no standard, and qualitative arguments [...]



Beyond Homemade Artificial Data Sets in HAIS 2009

Icon Written by admin on June 14, 2009 – 12:07 pm

Find below the presentation of the paper Beyond Homemade Artificial Data Sets by Núria Macià, Albert Orriols-Puig, and Ester Bernadó-Mansilla in the 2009 Hybrid Artificial Intelligence Systems (HAIS’09).



Getting ready for HAIS 2009

Icon Written by admin on June 9, 2009 – 7:55 pm

Tomorrow, the international Hybrid Artificial Intelligence Systems conference (HAIS) gets started in Salamanca with the special session of Knowledge Extraction based on Evolutionary Learning (KEEL). In this special session, the following 14 papers that use evolutionary algorithms for different purposes in the field of machine learning will be presented:



Analysis and Improvement of the genetic discovery component of XCS

Icon Written by admin on June 3, 2009 – 3:52 pm

by Sergio Morales-Ortigosa, Albert Orriols-Puig, and Ester Bernadó-Mansilla. Special issue of Data Mining and Hybrid Intelligent Systems in the International Journal of Hybrid and Intelligent Systems,  [Publisher site] [Preprint - pdf]



Prof. Cirac interviewed about quantum physics and theory information

Icon Written by admin on May 26, 2009 – 3:25 pm

A few days ago, Prof. Cirac was interviewed in a Catalan TV channel about his work on quantum theory of information. Prof. Cirac explained the method based on quantum cryptography that he and his team have been developing during the last few years, which makes sure that the information can be neither intercepted nor decrypted. [...]



Genetic algorithms rediscover laws of physics

Icon Written by admin on April 7, 2009 – 9:03 am

Over the last few decades, it has been shown that GAs (and derivate methods such as GPs) are able to solve complex real-world problems and rediscover engineering and scientific findings which were originally deduced after many years of investigation. Recently, Hod Lipson and Michael Schmidt have provided the scientific community with another cool [...]



Wolfram|Alpha is going life in two months

Icon Written by admin on March 21, 2009 – 12:07 pm

Some time ago I was told about the project Wolfram|Alpha by the creator of Mathematica and the author of a new kind of science (NKS), Stephen Wolfram. This project aims at going beyond the typical process of search engines by proposing a system that computes the answers of user questions. That is, instead of [...]



The next generation of neural networks

Icon Written by admin on March 16, 2009 – 9:57 am

Some few days ago, while preparing my lectures about neural networks, I ran into the video “The next generation of neural networks” by Geoffrey Hinton, one of the pioneers in machine learning and in the field of neural networks in particular.
Hinton starts the talk by presenting the first generation of neural networks, which included systems [...]