An improved parameter tuning method for support vector machines

Authors:
Yong Quan;Jie Yang
Affiliations:
Inst. of Image Processing & Pattern Recognition, Shanghai Jiaotong Univ. Shanghai, People's Republic of China;Inst. of Image Processing & Pattern Recognition, Shanghai Jiaotong Univ. Shanghai, People's Republic of China
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

Support vector machines (SVMs) is a very important tool for data mining. However, the problem of tuning parameters manually limits its application in practical environment. In this paper, under analyzing the limitation of these existing approaches, a new methodology to tuning kernel parameters, based on the computation of the gradient of penalty function with respect to the RBF kernel parameters, is proposed. Simulation results reveal the feasibility of this new approach and demonstrate an improvement of generalization ability.