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• W2039094037 abstract "Abstract Feedforward neural networks trained to solve classification problems define an approximation of the conditional probabilities P(Ci,|x) if the output units correspond to categories Ci. The present paper shows that if a least mean squared error cost function is minimised during training phase, the resulting approximation of the P(Ci|x)s is poor in the ranges of the input variable x where the conditional probabilities take on very low values. The use of the Kullback-Leibler distance measure is proposed to overcome this limitation; a cost function derived from this information theoretic measure is defined and a computationally light training procedure is derived in the case of binary classification problems. The effectiveness of the proposed procedure is verified by means of comparative experiments." @default.
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• W2039094037 date "1996-10-01" @default.
• W2039094037 modified "2023-09-27" @default.
• W2039094037 title "Improved binary classification performance using an information theoretic criterion" @default.
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• W2039094037 doi "https://doi.org/10.1016/0925-2312(96)00025-2" @default.
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