Author:
Franco María A.,Krasnogor Natalio,Bacardit Jaume
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)
Reference29 articles.
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3. Bacardit J, Garrell JM (2003) Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Proceedings of the 6th International Workshop on Learning Classifier Systems
4. Bacardit J, Goldberg DE, Butz MV (2007) Improving the performance of a pittsburgh learning classifier system using a default rule. In: Learning classifier systems, revised selected papers of the international workshop on learning classifier systems 2003–2005. Springer, LNCS 4399, pp. 291–307
5. Bacardit J, Goldberg DE, Butz MV, Llorá X, Garrell JM (2004) Speeding-Up pittsburgh learning classifier systems: modeling time and accuracy. In: Parallel problem solving from nature—PPSN VIII, Lecture Notes in Computer Science, vol. 3242, chap. 103. Springer, Berlin, Heidelberg, pp 1021–1031. http://www.springerlink.com/content/66w8u56a61wntqa6
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