Discriminant analysis by a neural network with mahalanobis distance

Authors:
Yoshifusa Ito;Cidambi Srinivasan;Hiroyuki Izumi
Affiliations:
Department of Policy Science, Aichi-Gakuin University, Nisshin, Aichi-ken, Japan;Department of Statistics, University of Kentucky, Lexington, Kentucky;Department of Policy Science, Aichi-Gakuin University, Nisshin, Aichi-ken, Japan
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

We propose a neural network which can approximate Mahalanobis discriminant functions after being trained. It can be realized if a Bayesian neural network is equipped with two additional subnetworks. The training is performed sequentially and, hence, the past teacher signals need not be memorized. In this paper, we treat the two-category normal-distribution case. The results of simple simulations are included.