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Single Directional SMO Algorithm for Least Squares Support Vector Machines
Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can se...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590457/ https://www.ncbi.nlm.nih.gov/pubmed/23509447 http://dx.doi.org/10.1155/2013/968438 |
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author | Shao, Xigao Wu, Kun Liao, Bifeng |
author_facet | Shao, Xigao Wu, Kun Liao, Bifeng |
author_sort | Shao, Xigao |
collection | PubMed |
description | Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones. |
format | Online Article Text |
id | pubmed-3590457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-35904572013-03-18 Single Directional SMO Algorithm for Least Squares Support Vector Machines Shao, Xigao Wu, Kun Liao, Bifeng Comput Intell Neurosci Research Article Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones. Hindawi Publishing Corporation 2013 2013-02-18 /pmc/articles/PMC3590457/ /pubmed/23509447 http://dx.doi.org/10.1155/2013/968438 Text en Copyright © 2013 Xigao Shao et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Shao, Xigao Wu, Kun Liao, Bifeng Single Directional SMO Algorithm for Least Squares Support Vector Machines |
title | Single Directional SMO Algorithm for Least Squares Support Vector Machines |
title_full | Single Directional SMO Algorithm for Least Squares Support Vector Machines |
title_fullStr | Single Directional SMO Algorithm for Least Squares Support Vector Machines |
title_full_unstemmed | Single Directional SMO Algorithm for Least Squares Support Vector Machines |
title_short | Single Directional SMO Algorithm for Least Squares Support Vector Machines |
title_sort | single directional smo algorithm for least squares support vector machines |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590457/ https://www.ncbi.nlm.nih.gov/pubmed/23509447 http://dx.doi.org/10.1155/2013/968438 |
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