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

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

*この研究の対応する著者

研究成果: Paper査読

10 被引用数 (Scopus)

抄録

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.

本文言語English
ページ67-76
ページ数10
出版ステータスPublished - 1995 1 1
外部発表はい
イベントProceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA
継続期間: 1995 8 311995 9 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

  • 信号処理
  • ソフトウェア
  • 電子工学および電気工学

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