Applications of feature space distance measures in training support vector classifiers

• Main Authors: Kuo-Ping Wu, 吳國賓
• Other Authors: 王勝德
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/66064360761867794430

博士 === 國立臺灣大學 === 電機工程學研究所 === 96 === Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. The most popular method to decide the parameters is the grid search method. In the training process, classifiers are trained with different parameter combinations, and only one of the classifiers is required for the testing process. This method can find a parameter combination with good generalization ability, while it makes the training process time-consuming. In this thesis we propose using separation indexes to estimate the generalization ability of the classifiers. These indexes are derived from the inter- and intra-cluster distances in the feature spaces. Calculating such indexes often costs much less computation time than training the corresponding SVM classifiers; thus the proper parameters can be chosen much faster. Experiment results show that some of the indexes can choose proper kernel parameters with which the testing accuracy of trained SVMs is competitive to the standard ones, and the training time can be significantly shortened.