The relationship between generalization error and the training sample number of SVM

  • Authors:

  • Junqing Bai;Guirong Yan;Wentao Mao

  • Affiliations:

  • The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China;The Key Laboratory of Strength and Vibration of Ministry of Education, Xi'an Jiaotong University, Xi'an, China

  • Venue:
  • ICNC'09 Proceedings of the 5th international conference on Natural computation
  • Year:
  • 2009

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Abstract

It is very important to construct the training set and determine the sample number in the regression problem. In this paper, a new idea of constructing the training set is elaborated. The key point of this idea is to choose the hyper-parameters before determining the training set. More importantly, a heuristic approach is proposed to select samples of support vector machine (SVM). Using these methods, the relationship between generalization error and the number of training samples on a given confidence level is computed. The empirical results on benchmark data (Boston Housing) and engineering data indicate that the proposed approach can give a reference to construct the proper training set. Moreover, the proposed approach has practical significance for other parametric learning machine.