| 摘要: |
| 对于大规模问题,分解方法是训练支撑向量机主要的一类方法.在很多分类问题中,有相当比例的支撑向量对应的拉格朗日乘子达到惩罚上界,而且在训练过程中到达上界的拉格朗日乘子变化平稳.利用这一统计特性,提出了一种有效的缓存策略来加速这类分解方法,并将其具体应用于Platt的贯序最小优化(sequential minimization optimization,简称SMO) 算法中.实验结果表明,改进后的SMO算法的速度是原有算法训练的2~3倍. |
| 关键词: 支撑向量机 模式分类 二次规划 缓存策略 贯序最小优化算法 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(60175006;60024301);国家创新研究群体科学基金项目(60024301) |
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| An Improved Sequential Minimization Optimization Algorithm for Support Vector Machine Training |
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SUN Jian,ZHENG Nan-ning,ZHANG Zhi-hua
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| Abstract: |
| The decomposition methods are main family to train SVM (support vector machine) for large-scale problem. In many pattern classification problems, most support vectors?Lagrangian multipliers are bound, and those multipliers change smoothly during training phases. Based on the facts, an efficient caching strategy is proposed to accelerate the decomposition methods in this paper. Platt抯 sequential minimization optimization (SMO) algorithm is improved by this caching strategy. The experimental results show that the modified algorithm can be 2~3 times faster than the classical SMO for large real-world data sets. |
| Key words: support vector machine pattern classification quadratic programming caching strategy sequential minimization optimization algorithm |
