Article: Discriminant analysis in pairwise kernel learning for SVM classification

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TitleDiscriminant analysis in pairwise kernel learning for SVM classification
AuthorsJiang, H1
Ching, WK1
Chu, D2
KeywordsClassification
Discriminant analysis
Kernel learning
Support vector machine
SVM
Issue Date2012
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra
CitationInternational Journal of Bioinformatics Research and Applications, 2012, v. 8 n. 3-4, p. 305-321 [How to Cite?]
DOI: http://dx.doi.org/10.1504/IJBRA.2012.048963
AbstractMultiple kernel learning arises when different types of kernels are employed simultaneously. In particular, in the situation that the data are from heterogeneous sources. In this study, we developed a new framework for determining the coefficients in learning pairwise kernels for classification in Support Vector Machines (SVM). The effectiveness of the proposed method was then demonstrated through the prediction of self-renewal and pluripotency mESCs stemness membership genes. It was also tested on the power of discrimination in DNA repair gene data. The promising formulation in learning coefficients for pairwise kernel learning was shown via experimental evaluation. This may provide a novel perspective for kernel learning in future applications.
ISSN1744-5485
2011 SCImago Journal Rankings: 0.069
DOIhttp://dx.doi.org/10.1504/IJBRA.2012.048963
DC Field
Value
dc.contributor.authorJiang, H
dc.contributor.authorChing, WK
dc.contributor.authorChu, D
dc.date.accessioned2012-09-20T07:56:20Z
dc.date.available2012-09-20T07:56:20Z
dc.date.issued2012
dc.description.abstractMultiple kernel learning arises when different types of kernels are employed simultaneously. In particular, in the situation that the data are from heterogeneous sources. In this study, we developed a new framework for determining the coefficients in learning pairwise kernels for classification in Support Vector Machines (SVM). The effectiveness of the proposed method was then demonstrated through the prediction of self-renewal and pluripotency mESCs stemness membership genes. It was also tested on the power of discrimination in DNA repair gene data. The promising formulation in learning coefficients for pairwise kernel learning was shown via experimental evaluation. This may provide a novel perspective for kernel learning in future applications.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationInternational Journal of Bioinformatics Research and Applications, 2012, v. 8 n. 3-4, p. 305-321 [How to Cite?]
DOI: http://dx.doi.org/10.1504/IJBRA.2012.048963
dc.identifier.doihttp://dx.doi.org/10.1504/IJBRA.2012.048963
dc.identifier.epage321
dc.identifier.hkuros208785
dc.identifier.issn1744-5485
2011 SCImago Journal Rankings: 0.069
dc.identifier.issue3-4
dc.identifier.pmid22961457
dc.identifier.scopuseid_2-s2.0-84866246773
dc.identifier.spage305
dc.identifier.urihttp://hdl.handle.net/10722/164183
dc.identifier.volume8
dc.languageeng
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra
dc.publisher.placeUnited Kingdom
dc.relation.ispartofInternational Journal of Bioinformatics Research and Applications
dc.rightsInternational Journal of Bioinformatics Research and Applications. Copyright © Inderscience Publishers.
dc.subjectClassification
dc.subjectDiscriminant analysis
dc.subjectKernel learning
dc.subjectSupport vector machine
dc.subjectSVM
dc.titleDiscriminant analysis in pairwise kernel learning for SVM classification
dc.typeArticle
Author Affiliations
  1. The University of Hong Kong
  2. National University of Singapore