Model Selection for SVM Based on Similarity Margin of Inner Product
By investigating the relationships among generalization performance, VC dimension and margin in SVM, this paper proposes a novel similarity margin of inner product. Then, the richness or flexibility of corresponding function class, along with estimative upper bound of relevant dimensionality, is analyzed in detail. Aiming at the demerit of difficult selection of optimal kernel function and its parameters in SVM, we discuss the improvement of generalization performance in terms of similarity margin of inner product and construct a model selection approach based on similarity margin of inner product, by which we could effectively avert such shortcomings as high computational costs and formulation complexity in traditional model selection methods. Experimental results on both artificial dataset and practical dataset show that our algorithm can find out preferable kernel parameter model, as well as maintain better classification accuracy, so it is more applicable.
Support Vector Machine Margin Model Selection Kernel Function
Zhu Jin Ying Chen Xiaoping Ma
The School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221116, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
1653-1657
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）