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Neural Discriminant Analysis

  • Jorge Ricardo Cuellar
  • Hans Ulrich Simon
Selected Papers Approximate Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 744)

Abstract

Statistical Discriminant Analysis is a classical technique in pattern matching with applications for classification problems and more general decision tasks. In this paper, we use a specific class of discriminant functions which we call product discriminant functions, or simply PDF's. Our main results for PDF's are the following:
  • They are quite expressive, e.g., probability distributions defined by Chow-Expansions, Unique Probabilistic Automata or Unique Markov Models can also succinctly be written as PDF's.

  • It is possible to obtain with high confidence almost optimal decisions for classification problems which can be modelled by PDF's. The number of training examples needed for that is bounded by a polynomial of low degree (in the relevant parameters).

  • The evaluation of the training examples can be implemented on shallow neural nets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Jorge Ricardo Cuellar
    • 1
  • Hans Ulrich Simon
    • 2
  1. 1.Siemens Corporate ResearchMünchen 83
  2. 2.Fachbereich InformatikUniversität DortmundDortmund 50

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