Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||Two-pattern Classification And Feature Extraction Based On Minimum Error Decision Boundary Using Neural Networks|
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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