关键词: 模式识别, 支持向量数据描述, 多核方法, 最优超球半径, 参数选择

Abstract: Kernel function, penalty factor, kernel function parameters are important factors influencing the classification effects of the Support Vector Data Description method（SVDD）. This paper studies the Multiple Kernel Support Vector Data Description（MKSVDD） classification method, furthermore, gives the classification implementation steps of multi-
kernel support vector data description method. It analyzes the impact of the penalty factor and kernel function parameters on classification results, mainly discusses the influence of the weight of multi-kernel function on boundary distribution of SVDD based on banana data set. The simulation results reveal that, compared with the single kernel support vector data description method of classification, multi-kernel support vector data description method of classification has better performance, provides the reference of parameters selection in the practical application.

Key words: pattern recognition, support vector data description, multi-kernel method, optimal hyper-sphere radius, parameter selection