LINEAR DECISION FUNCTIONS FOR PATTERN CLASSIFICATION

Sik-Sang Yau, TT LIN TT

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Linear decision functions approximating the optimal decision functions in the least-mean-square-error sense are determined. By choosing a particular form of optimal decision functions, it is possible to evaluate the linear decision functions explicitly. It is found that this approach embodies some existing methods for evaluating linear discriminant functions, and is applicable to more general performance criteria. An iterative algorithm which improves the performance of the linear classifier based on linear decision functions substantially is proposed.

Original languageEnglish (US)
Title of host publicationUnknown Host Publication Title
Pages448-452
Number of pages5
StatePublished - 1968
Externally publishedYes
EventPrinceton Univ-2nd Annual Princeton Conference on Information Sciences & Systems-Proc -
Duration: Mar 25 1968Mar 26 1968

Other

OtherPrinceton Univ-2nd Annual Princeton Conference on Information Sciences & Systems-Proc
Period3/25/683/26/68

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yau, S-S., & LIN TT, TT. (1968). LINEAR DECISION FUNCTIONS FOR PATTERN CLASSIFICATION. In Unknown Host Publication Title (pp. 448-452)