A Linear-RBF Multikernel SVM to Classify Big Text Corpora.
Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 15 |
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| Autores principales: | , , |
| Formato: | equations & formulas research Journal Article |
| Publicado: |
Wiley-Blackwell
3/23/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273760&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273760 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/23/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273760 109273760 109273760 10.1155/2015/878291 109273760 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: A Linear-RBF Multikernel SVM to Classify Big Text Corpora. aug: au: Romero, R. Iglesias, E. L. Borrajo, L. affil: Department of Computer Science, Higher Technical School of Computer Engineering, University of Vigo, 32004 Ourense, Spain sug: subj: Databases Technology Access to Information Evaluation Data Analysis Methods Human Models, Statistical Funding Source ab: Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers. pubtype: Academic Journal doctype: equations & formulas research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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