A combined SVM and LDA approach for classification

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper describes a new large margin classifier, named SVM/LDA. This classifier can be viewed as an extension of support vector machine (SVM) by incorporating some global information about the data. The SVM/LDA classifier can be also seen as a generalization of linear discriminant analysis (LDA) by incorporating the idea of (local) margin maximization into standard LDA formulation. We show that existing SVM software can be used to solve the SVM/LDA formulation. We also present empirical comparisons of the proposed algorithm with SVM and LDA using both synthetic and real world benchmark data.

Original languageEnglish (US)
Pages1455-1459
Number of pages5
DOIs
StatePublished - 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period7/31/058/4/05

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

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