A subclass model for non-linear pattern classification

Qiuming Zhu, Yao Cai

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper describes a pattern classification model called "classification on subclasses". The model and its computation scheme are based on the theoretic foundation of minimizing the cross-entropy of the distribution functions that bear considerable complexity and non-linearity. In this model, pattern classes are configured and described by a number of subclasses each associated with a distribution formulated according to the regularization principle. This modeling technique provides a simplified solution to a group of non-linear pattern classification problems. Simulation shows a high classification rate on pattern samples with complex distributions.

Original languageEnglish (US)
Pages (from-to)19-29
Number of pages11
JournalPattern Recognition Letters
Volume19
Issue number1
DOIs
StatePublished - May 1998

Fingerprint

Pattern recognition
Distribution functions
Entropy

Keywords

  • Cross-entropy
  • Non-linearity
  • Pattern classification
  • Regularization
  • Subclasses

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

A subclass model for non-linear pattern classification. / Zhu, Qiuming; Cai, Yao.

In: Pattern Recognition Letters, Vol. 19, No. 1, 05.1998, p. 19-29.

Research output: Contribution to journalArticle

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