| 引用本文: | 李仁兵,李艾华,白向峰,蔡艳平,王德生.支持向量机的进化多核设计[J].控制理论与应用,2011,28(6):793~798.[点击复制] |
| LI Ren-bing,LI Ai-hua,BAI Xiang-feng,CAI Yan-ping,WANG De-sheng.Evolutionary multiple kernels design for support vector machines[J].Control Theory and Technology,2011,28(6):793~798.[点击复制] |
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| 支持向量机的进化多核设计 |
| Evolutionary multiple kernels design for support vector machines |
| 摘要点击 2454 全文点击 3083 投稿时间：2010-08-29 修订日期：2010-11-11 |
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| DOI编号 10.7641/j.issn.1000-8152.2011.6.CCTA100994 |
| 2011,28(6):793-798 |
| 中文关键词 进化多核 遗传程序设计 支持向量机 核函数 |
| 英文关键词 evolutionary multiple kernels genetic programming support vector machines kernel function |
| 基金项目 |
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| 中文摘要 |
| 为提高支持向量机分类精度, 提出一种基于遗传程序设计的进化多核算法. 算法中每个个体表示一个多核函数, 并采用树形结构进行编码, 增强了多核函数的非线性; 初始种群由生长法产生, 经过遗传操作后得到适合具体问题的进化多核函数. 遗传程序设计的全局搜索性能使得算法设计不需要先验知识. 与单核函数及其他多核函数的对比实验结果表明, 进化多核有效提高了支持向量机分类性能. |
| 英文摘要 |
| To boost the classification accuracy of support-vector-machines(SVM), we propose an algorithm with evolutionary multiple kernels(EMK), based on the genetic programming(GP). In this algorithm, each individual represents a multiple kernel function, and is encoded by the tree-structure for enhancing the non-linearity of the multiple kernel function. Grow method is applied to initialize the GP population, from which the EMK adapting to practical problems is obtained by genetic operations. No priori knowledge is required due to the global search of GP. Comparisons of experimental results of EMK with the single kernel function and other multiple kernel functions show that EMK effectively improves the classification performance of SVM. |
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