Competition-based supervised learning algorithm for nonlinear discriminant functions

S. Y. Kung, W. D. Mao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A basic competition-based model is the now-classic perceptron net using linear discriminant functions. The competition-based learning is extended to the general cases of nonlinear discriminant functions. Generalized perceptron learning rules for the binary-classification and multiple-classification cases are proposed. The convergency properties of the general perceptrons are established. Simulation results on texture classification applications are provided.

Original languageEnglish (US)
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherPubl by IEEE
Pages1073-1076
Number of pages4
ISBN (Print)0780300033
DOIs
StatePublished - Jan 1 1991
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: May 14 1991May 17 1991

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2
ISSN (Print)0736-7791

Other

OtherProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period5/14/915/17/91

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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