| Fast Training of Support Vector Machines Using Error-Center-Based Optimization |
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| 作者姓名： | L. Meng Q. H. Wu |
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| 作者单位： | L. Meng，Q. H. Wu Department of Electrical Engineering and Electronics，The University of Liverpool，Liverpool，L69 3GJ，UK |
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| 摘 要： | 1 Introduction Based on recent advances in statistical learning theory, Support Vector Machines (SVMs) compose a new class of learning system for pattern classification. Training a SVM amounts to solving a quadratic pro- gramming (QP) problem with a dense matrix. Stan- dard QP solvers require the full storage of this matrix, and their e?ciency lies in its sparseness, which make its application to SVM training with large training sets intractable. The SVM, pioneered by Vapnik and his te…
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| 收稿时间： | 5 November 2003 |
| 修稿时间： | 2004-6-1 |
Fast training of Support Vector Machines using error-center-based optimization |
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| L. Meng,Q. H. Wu.Fast Training of Support Vector Machines Using Error-Center-Based Optimization[J].International Journal of Automation and computing,2005,2(1):6-12. |
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| Authors: | L?Meng Email author" target="_blank">Q?H?WuEmail author |
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| Affiliation: | (1) Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, L69 3GJ, UK |
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| Abstract: | This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments with various training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard quadratic programming (QP) techniques. |
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| Keywords: | Support vector machines quadratic programming pattern classification machine learning |
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