Data Mining in Learning Classifier Systems: Comparing XCS with GAssist

Author:

Bacardit Jaume,Butz Martin V.

Publisher

Springer Berlin Heidelberg

Reference12 articles.

1. Lecture Notes in Computer Science;J. Bacardit,2004

2. Bacardit, J.: Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain (2004)

3. Bacardit, J., Garrell, J.M.: 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, Springer, Heidelberg (in press, 2003)

4. Bacardit, J., Garrell, J.M.: Incremental learning for pittsburgh approach classifier systems. In: Proceedings of the “Segundo Congreso Españl de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados”, pp. 303–311 (2003)

5. Bernadó, E., Llorà, X., Garrell, J.M.: XCS and GALE: a comparative study of two learning classifier systems with six other learning algorithms on classification tasks. In: Fourth International Workshop on Learning Classifier Systems - IWLCS-2001, pp. 337–341 (2001)

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