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dc.contributor.authorJia, W
dc.contributor.authorLiang, J
dc.contributor.authorZhang, M
dc.contributor.authorYe, X
dc.description.abstractTraditional Support Vector Regression (SVR) Machine acts as approximating a regression function. This paper, however, proposes a novel multi-class classification approach based on the SVR framework, called Support Vector Regression Machine with Consistency (SVRC). The contributions of this paper are: (1) To implement multi-class classification task, were place the margin term with its l1 norm in the SVR framework; (2)To make the training data within the same class possess approximate contributions for the test sample reconstruction and thus improve the robustness, we construct a consistent matrix employing the class information and introduce the penalty term using it; (3) To pay more attention to using fewer possible classes to represent the test sample, and thus improve the accuracy of the test sample reconstruction, we utilize the corresponding local neighborhood relationship of the test sample to design a selection matrix. Experimental results demonstrate that the performance of the proposed method is much better than that of some existing multi-class classification approaches.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States of America
dc.relation.ispartofconferencenameICSPCC 2015
dc.relation.ispartofconferencetitle2015 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015
dc.relation.ispartoflocationNingbo, China
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.titleMulti-class classification using support vector regression machine with consistency
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorZhang, Lena

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    Contains papers delivered by Griffith authors at national and international conferences.

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