Actas de congresos
Two-pattern Classification And Feature Extraction Based On Minimum Error Decision Boundary Using Neural Networks
Registro en:
0780320158; 9780780320154
Ieee International Symposium On Information Theory - Proceedings. , v. , n. , p. 173 - , 1994.
21578095
10.1109/ISIT.1994.394799
2-s2.0-84894346362
Autor
Lee L.L.
Institución
Resumen
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.
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