The Relationship between Generalization Error and the Training Sample Number of SVM

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.