The main goal in pattern recognition is to be able to recognize interest patterns, although these patterns might be altered in some way. Associative memories is a branch in AI that obtains one generalization per class from the initial data set. The main problem is that when generalization is performed much information is lost. This is mainly due to the presence of outliers and pattern distribution in space. It is believed that one generalization is not sufficient to keep the information necessary to achieve a good performance in the recall phase. This paper shows a way to prevent information loss and make more significative learning allowing better recalling results. © 2012 Springer-Verlag.
CITATION STYLE
Armando, C. S. E., Cornelio, Y. M., & Jesus, F. N. (2012). Significative learning using Alpha-Beta associative memories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 535–542). https://doi.org/10.1007/978-3-642-33275-3_66
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