File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Discriminant analysis in pairwise kernel learning for SVM classification

TitleDiscriminant analysis in pairwise kernel learning for SVM classification
Authors
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
Citation
International Journal of Bioinformatics Research and Applications, 2012, v. 8 n. 3-4, p. 305-321 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/164183
ISSN
2014 SCImago Journal Rankings: 0.170

 

DC FieldValueLanguage
dc.contributor.authorJiang, Hen_US
dc.contributor.authorChing, WKen_US
dc.contributor.authorChu, Den_US
dc.date.accessioned2012-09-20T07:56:20Z-
dc.date.available2012-09-20T07:56:20Z-
dc.date.issued2012en_US
dc.identifier.citationInternational Journal of Bioinformatics Research and Applications, 2012, v. 8 n. 3-4, p. 305-321en_US
dc.identifier.issn1744-5485-
dc.identifier.urihttp://hdl.handle.net/10722/164183-
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.languageengen_US
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra-
dc.relation.ispartofInternational Journal of Bioinformatics Research and Applicationsen_US
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 classificationen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK: wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJBRA.2012.048963-
dc.identifier.pmid22961457-
dc.identifier.scopuseid_2-s2.0-84866246773-
dc.identifier.hkuros208785en_US
dc.identifier.volume8en_US
dc.identifier.issue3-4-
dc.identifier.spage305en_US
dc.identifier.epage321en_US
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats