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| 基于混合核函数的PSO-SVM分类算法 | |
| 引用本文： | 刘春卫,罗健旭. 基于混合核函数的PSO-SVM分类算法[J]. 医学教育探索, 2014, 0(1): 96-101 |
| 作者姓名： | 刘春卫 罗健旭 |
| 作者单位： | 华东理工大学信息科学与工程学院， 上海 200237;华东理工大学信息科学与工程学院， 上海 200237 |
| 基金项目： | 上海市自然科学基金（12ZR1408200）；中央高校基本科研业务费专项资金 |
| 摘 要： | 在数据挖掘中，支持向量机是被广泛应用的一种分类算法，其核函数的选择及参数的设定没有有效的标准。本文采用混合核函数构造兼顾学习能力和泛化性能的支持向量机算法，并利用粒子群算法来确定支持向量机的参数。应用基于混合核函数的PSO SVM算法对一个经典的分类测试数据集进行分类，将该算法与单一核函数支持向量机算法的分类结果进行比较，结果表明所提出的算法的分类性能有明显提升。 |
| 关 键 词： | 支持向量机； 混合核函数； 粒子群优化 |
| 收稿时间： | 2013-06-19 |
A PSO SVM Classifier Based on Hybrid Kernel Function |
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| LIU Chun-wei and LUO Jian-xu. A PSO SVM Classifier Based on Hybrid Kernel Function[J]. Researches in Medical Education, 2014, 0(1): 96-101 | |
| Authors: | LIU Chun-wei and LUO Jian-xu |
| Affiliation: | School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China |
| Abstract: | Abstract: Support vector machine(SVM) has been widely used in data mining as a classification algorithm. However, there is no effective standard in choosing its kernel function and setting the related parameters. In order to reach both stronger learning ability and generalization ability, this paper poposes a hybrid kernel function, in which the parameters of SVM are optimized by means of PSO(particle swarm optimization). The comparison is made on a classical classification problem, which is shown from the results that the proposed algorithm has better performance than the SVM based on single kernel function. |
| Keywords: | support vector machine mixed kernel function particle swarm optimization |
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