A New Algorithm for Learning Mahalanobis Discriminant Functions by a Neural Network

  • Yoshifusa Ito
  • Hiroyuki Izumi
  • Cidambi Srinivasan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7063)

Abstract

It is well known that a neural network can learn Bayesian discriminant functions. In the two-category normal-distribution case, a shift by a constant of the logit transform of the network output approximates a corresponding Mahalanobis discriminant function [7]. In [10], we have proposed an algorithm for estimating the constant, but it requires the network to be trained twice, in one of which the teacher signals must be shifted by the mean vectors. In this paper, we propose a more efficient algorithm for estimating the constant with which the network is trained only once.

Keywords

Neural Network Discriminant Function Mahalanobis Distance Output Unit Network Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yoshifusa Ito
    • 1
  • Hiroyuki Izumi
    • 2
  • Cidambi Srinivasan
    • 3
  1. 1.School of MedicineAichi Medical UniversityNagakuteJapan
  2. 2.Department of Policy ScienceAichi-Gakuin UniversityNisshinJapan
  3. 3.Department of StatisticsUniversity of KentuckyLexingtonUSA

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