Simultaneous design of feature extractor and pattern classifier using the minimum classification error training algorithm

K. K. Paliwal, M. Bacchiani, Y. Sagisaka

Research output: Contribution to conferencePaper

10 Citations (Scopus)

Abstract

Recently, a minimum classification error training algorithm has been proposed for minimizing the misclassification probability based on a given set of training samples using a generalized probabilistic descent method. This algorithm is a type of discriminative learning algorithm, but it approaches the objective of minimum classification error in a more direct manner than the conventional discriminative training algorithms. We apply this algorithm for simultaneous design of feature extractor and pattern classifier, and demonstrate some of its properties and advantages.

Original languageEnglish
Pages67-76
Number of pages10
Publication statusPublished - 1995 Jan 1
Externally publishedYes
EventProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA
Duration: 1995 Aug 311995 Sep 2

Other

OtherProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)
CityCambridge, MA, USA
Period95/8/3195/9/2

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Paliwal, K. K., Bacchiani, M., & Sagisaka, Y. (1995). Simultaneous design of feature extractor and pattern classifier using the minimum classification error training algorithm. 67-76. Paper presented at Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95), Cambridge, MA, USA, .