| 支持向量机学习算法的研究现状与展望 |
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| 引用本文： | 崔和,龙玉峰.支持向量机学习算法的研究现状与展望[J].信息与电子工程,2008,6(5). |
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| 作者姓名： | 崔和 龙玉峰 |
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| 作者单位： | 1. 中国人民解放军92857部队,北京,100073
2. 中国人民解放军92154部队,山东,烟台,264001 |
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| 摘 要： | 回顾了支持向量机理论的发展历程,介绍了支持向量机的标准训练算法及其分解算法、变形算法、几何算法以及多类分类算法,重点描述了最小二乘支持向量机算法,总结了支持向量机理论及其应用的现状,对支持向量机的未来发展方向进行了展望。
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| 关 键 词： | 支持向量机 特征空间 最小二乘法 |
Status Quo and Expectation of Support Vector Machine Learning Algorithm |
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| CUI He,LONG Yu-feng.Status Quo and Expectation of Support Vector Machine Learning Algorithm[J].information and electronic engineering,2008,6(5). |
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| Authors: | CUI He LONG Yu-feng |
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| Abstract: | The development history of Support Vector Machine(SVM) is reviewed. The standard training algorithm,decomposition algorithm,transmutation algorithm,geometry algorithm and multi-class classifier algorithm of SVM are introduced. The Least Square(LS) SVM algorithm is described in detail. The status quo of SVM theory and its applications are summarized and the future development direction of SVM theory is expected. |
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| Keywords: | Support Vector Machine character space Least Square |
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