| 基于活跃集迭代法的支持向量机快速增量学习算法 |
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| 引用本文： | 陶亮.基于活跃集迭代法的支持向量机快速增量学习算法[J].系统仿真学报,2006,18(11):3305-3308,3312. |
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| 作者姓名： | 陶亮 |
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| 作者单位： | 安徽大学计算机科学与技术学院,安徽,合肥,230039 |
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| 基金项目： | 国家自然科学基金;安徽大学校科研和教改项目;创新团队基金 |
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| 摘 要： | 介绍了一种新的支持向量机（SVM），其优化问题的对偶问题为具有简单界约束的凸二次规划问题：探讨了将活跃集迭代法运用于这种SVM的学习算法以及初始活跃集的选取问题；针对增量学习和大规模学习问题，提出了基于活跃集迭代法的SVM快速增量学习算法；实验验证了算法的有效性。
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| 关 键 词： | 支持向量机 增量学习 活跃集迭代法 凸二次规划 |
| 文章编号： | 1004-731X（2006）11-3305-04 |
| 收稿时间： | 2005-08-30 |
| 修稿时间： | 2005-08-302005-11-28 |
Fast Incremental SVM Learning Algorithm Based on Active Set Iterations |
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| TAO Liang.Fast Incremental SVM Learning Algorithm Based on Active Set Iterations[J].Journal of System Simulation,2006,18(11):3305-3308,3312. |
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| Authors: | TAO Liang |
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| Institution: | School of Computer Science and Technology, Anhui University, Hefei 230039, China |
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| Abstract: | A new support vector machine (SVM) was introduced. What is unusual for the SVM is that the dual problem for the constrained optimization of the SVM is a convex quadratic problem with simple bound constraints. The active set iteration method for this quadratic problem was applied as a learning algorithm for the SVM, and the selection of the initial active set was discussed. For incremental learning and large-scale learning problems, a fast incremental learning algorithm for the SVM was proposed. Some experiments show the efficiency of the proposed algorithm. |
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| Keywords: | support vector machines incremental learning active set iterations convex quadratic optimization |
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