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基于Fisher准则和最大熵原理的SVM核参数选择方法

DOI: 10.13195/j.kzyjc.2013.1071, PP. 1991-1996

Keywords: 支持向量机,核函数,参数选择,Fisher,准则,最大熵原理,粒子群优化算法

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Abstract:

针对支持向量机(SVM)核参数选择困难的问题,提出一种基于Fisher准则和最大熵原理的SVM核参数优选方法.首先,从SVM分类器原理出发,提出SVM核参数优劣的衡量标准;然后,根据此标准利用Fisher准则来优选SVM核参数,并引入最大熵原理进一步调整算法的优选性能.整个模型采用粒子群优化算法(PSO)进行参数寻优.UCI标准数据集实验表明了所提方法具有良好的参数选择效果,优选出的核参数能够使SVM具有较高的泛化性能.

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