Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/96315
Type: Artigo de evento
Title: Two-pattern Classification And Feature Extraction Based On Minimum Error Decision Boundary Using Neural Networks
Author: Lee L.L.
Abstract: 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.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/ISIT.1994.394799
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84894346362&partnerID=40&md5=bfb07c557815ae8256b37f28e83c7c60
Date Issue: 1994
Appears in Collections:Unicamp - Artigos e Outros Documentos

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