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- Publisher Website: 10.1504/IJBRA.2012.048963
- Scopus: eid_2-s2.0-84866246773
- PMID: 22961457
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Article: Discriminant analysis in pairwise kernel learning for SVM classification
Title | Discriminant analysis in pairwise kernel learning for SVM classification |
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Authors | |
Keywords | Classification Discriminant analysis Kernel learning Support vector machine SVM |
Issue Date | 2012 |
Publisher | Inderscience 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? |
Abstract | Multiple 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 Identifier | http://hdl.handle.net/10722/164183 |
ISSN | 2023 SCImago Journal Rankings: 0.138 |
DC Field | Value | Language |
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dc.contributor.author | Jiang, H | en_US |
dc.contributor.author | Ching, WK | en_US |
dc.contributor.author | Chu, D | en_US |
dc.date.accessioned | 2012-09-20T07:56:20Z | - |
dc.date.available | 2012-09-20T07:56:20Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | International Journal of Bioinformatics Research and Applications, 2012, v. 8 n. 3-4, p. 305-321 | en_US |
dc.identifier.issn | 1744-5485 | - |
dc.identifier.uri | http://hdl.handle.net/10722/164183 | - |
dc.description.abstract | Multiple 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.language | eng | en_US |
dc.publisher | Inderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra | - |
dc.relation.ispartof | International Journal of Bioinformatics Research and Applications | en_US |
dc.rights | International Journal of Bioinformatics Research and Applications. Copyright © Inderscience Publishers. | - |
dc.subject | Classification | - |
dc.subject | Discriminant analysis | - |
dc.subject | Kernel learning | - |
dc.subject | Support vector machine | - |
dc.subject | SVM | - |
dc.title | Discriminant analysis in pairwise kernel learning for SVM classification | en_US |
dc.type | Article | en_US |
dc.identifier.email | Ching, WK: wching@hku.hk | en_US |
dc.identifier.authority | Ching, WK=rp00679 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1504/IJBRA.2012.048963 | - |
dc.identifier.pmid | 22961457 | - |
dc.identifier.scopus | eid_2-s2.0-84866246773 | - |
dc.identifier.hkuros | 208785 | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.issue | 3-4 | - |
dc.identifier.spage | 305 | en_US |
dc.identifier.epage | 321 | en_US |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1744-5485 | - |