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| 引用本文: | 任文进,钟清流. 基于混沌粒子群的支持向量机参数优化[J]. 科学技术与工程, 2007, (18): 4597-4600 |
| . Parameter Optimization of Support Vector Machine Based on Chaos-Particle Swarm Optimization[J]. Science Technology and Engineering, 2007, (18): 4597-4600 |
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| 基于混沌粒子群的支持向量机参数优化 |
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任文进,钟清流
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| 摘要: |
| 支持向量机的参数选择决定了其学习性能和泛化能力，由于在参数的选择范围内可选择的数量是无穷的，在多个参数中盲目搜索最优参数是需要极大的时间代价，并且很难逼近最优。基于此，提出一种基于混沌粒子群的支持向量机参数选择算法。混沌粒子群优化算法是一种全局搜索方法，在选取SVM参数时，不必考虑模型的复杂度和变量维数．仿真表明，混沌粒子群优化算法是选取SVM参数的有效方法，可以取得令人满意的效果。 |
| 关键词: 支持向量机 混沌粒子群 参数选择 |
| DOI： |
| 分类号:TP183 |
| 基金项目: |
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| Parameter Optimization of Support Vector Machine Based on Chaos-Particle Swarm Optimization |
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REN Wen-jin ZHONG Qing-liu
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| Abstract: |
| The support vector machine parameter decides its study performance and exudes the ability. As the parameter choice is infinite, the parameter chioce needs enormous time, and is very difficult to approach superiorly. So, a new kind parameter optimization of support Vector machine is proposed Based on chaos-particle swarm optimization, chaos-particle swarm optimization is a overall situation reconnaissance method, does not need to consider the model complex and the variable dimension. Simulation results show that the classifier has stronger ability to distinguish garbage messages. |
| Key words: SVM CPSO parameter optimization |
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| 参考文献(共5条): | | [1]Vapnik V N.Estimation of Dependencies Based on Empirical Data.Berlin:Springer-Verlag,1982 | | [2]Graepel T.Classification on Proximity Data with LP-machine.Ninth International Conference on Artificial Neural Networks IEEE.London,1999,304-309 | | [3]Cristianini N,Shawe T J,Kandola J,et al.On Kernel Target Alignment.Neural Information Processing Systems.Cambridge,MA:MIT Press,2002,367-373 | | [4]Eberhart R C,Kennedy J.A New Optimizer Using Particles Swam Theory.International Symposium on Micro-Machine and Human Science.Nagoya,1995,39-43 | | [5]MurthyS,haDA.UCIrepositoryofmachinelearningdatatables[DB/OL]Available:http://www.ics.uci.edu/～mlearn/ |
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