| dc.creator | Lee L.L. | |
| dc.date | 1994 | |
| dc.date | 2015-06-26T17:27:34Z | |
| dc.date | 2015-11-26T14:22:16Z | |
| dc.date | 2015-06-26T17:27:34Z | |
| dc.date | 2015-11-26T14:22:16Z | |
| dc.date.accessioned | 2018-03-28T21:24:07Z | |
| dc.date.available | 2018-03-28T21:24:07Z | |
| dc.identifier | 0780320158; 9780780320154 | |
| dc.identifier | Ieee International Symposium On Information Theory - Proceedings. , v. , n. , p. 173 - , 1994. | |
| dc.identifier | 21578095 | |
| dc.identifier | 10.1109/ISIT.1994.394799 | |
| dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-84894346362&partnerID=40&md5=bfb07c557815ae8256b37f28e83c7c60 | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/96315 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/96315 | |
| dc.identifier | 2-s2.0-84894346362 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1244770 | |
| dc.description | A new method is proposed for two pattern classification and feature extraction based directly on an optimum decision boundary using neural networks (NN). The proposed approach has several desirable properties: (1) it predicts an optimum decision boundary which provides a classification accuracy at least as good as as that of an optimum global decision hyperplane; (2) it extracts optimum discrimination features even though the joint probability distribution of features is unknown; and (3) it determines the minimum number of discriminating features. © 1994 IEEE. | |
| dc.description | | |
| dc.description | | |
| dc.description | 173 | |
| dc.description | | |
| dc.language | en | |
| dc.publisher | | |
| dc.relation | IEEE International Symposium on Information Theory - Proceedings | |
| dc.rights | fechado | |
| dc.source | Scopus | |
| dc.title | Two-pattern Classification And Feature Extraction Based On Minimum Error Decision Boundary Using Neural Networks | |
| dc.type | Actas de congresos | |
