Title :
Learning Curves of Support Vector Machines
Author_Institution :
Graduate Sch. of Informatics, Kyoto Univ.
Abstract :
A support vector machines (SVM) is known as a pattern classifier with a high generalization ability and one of its advantages is that the generalization ability is theoretically guaranteed. However, many of the analyses are given in the framework of the PAC learning and the error-bounds are rather loose than the practical generalization error. In this paper, we present some studies on the average generalization error of SVMs, which is a more practical criterion for generalization ability
Keywords :
learning (artificial intelligence); pattern classification; support vector machines; generalization ability; learning curves; pattern classifier; support vector machines; Equations; Error correction; Informatics; Kernel; Machine learning; Multilayer perceptrons; Radial basis function networks; Support vector machine classification; Support vector machines; US Department of Transportation;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614958
