Discriminant Analysis by a Neural Network with Mahalanobis Distance

  • Yoshifusa Ito
  • Cidambi Srinivasan
  • Hiroyuki Izumi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


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.


Neural Network Discriminant Analysis Discriminant Function Prior Probability Mahalanobis Distance 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yoshifusa Ito
    • 1
  • Cidambi Srinivasan
    • 2
  • Hiroyuki Izumi
    • 1
  1. 1.Department of Policy ScienceAichi-Gakuin UniversityNisshin, Aichi-kenJapan
  2. 2.Department of StatisticsUniversity of KentuckyLexingtonUSA

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