Abstract
Fuzzy ARTMAP is capable of incrementally learning interpretable rules. To remove unused or inaccurate rules, a rule pruning method has been proposed in the literature. This paper addresses its limitations when incremental learning is used, and modifies it so that it does not need to store previously learnt samples. Experiments show a better performance, especially in concept drift problems. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Andrés-Andrés, A., Gómez-Sánchez, E., & Bote-Lorenzo, M. L. (2005). Incremental rule pruning for fuzzy ARTMAP neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 655–660). https://doi.org/10.1007/11550907_104
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
