Investigation on Generalization Errors Assessment Algorithms of SVM Classification
Wei Peng · Computer Engineering and Applications Journal · 2006
In order to select a good hypothesis(or model) from a collection of possible models,one has to assess the generalization performance of the hypothesis which returned by a learner that is bound to use some particular model.Several methods for estimating the generalization error of the hypotheses with least test error in the model are intro-duced in this paper,and the advantages and disadvantages of the error estimators are also analyzed in detail.The experi-mental results show different value obtained from cross-validation,RM-bounds and eα-estimator algorithms.The results also show that different problems can choose variant error estimation functions to predict the optimal parameters in the selected model.