| Título |
Significative learning using Alpha-Beta associative memories |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 |
| Resumen |
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. |
| Observaciones |
DOI: 10.1007/978-3-642-33275-3_66 |
| Lugar |
Buenos Aires |
| País |
Argentina |
| No. de páginas |
535-542 |
| Vol. / Cap. |
Volume 7441 LNCS, 2012 |
| Inicio |
2012-09-03 |
| Fin |
2012-09-06 |
| ISBN/ISSN |
978-364233274-6 |
