https://scholars.lib.ntu.edu.tw/handle/123456789/352571
標題: | Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space | 作者: | Kuo-Ping Wu SHENG-DE WANG |
關鍵字: | Inter-cluster distances; Kernel parameters; Support vector machines; SVM | 公開日期: | 五月-2009 | 卷: | 42 | 期: | 5 | 起(迄)頁: | 710-717 | 來源出版物: | Pattern Recognition | 摘要: | Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. A popular method to deciding the kernel parameters is the grid search method. In the training process, classifiers are trained with different kernel parameters, and only one of the classifiers is required for the testing process. This makes the training process time-consuming. In this paper we propose using the inter-cluster distances in the feature spaces to choose the kernel parameters. Calculating such distance costs much less computation time than training the corresponding SVM classifiers; thus the proper kernel parameters can be chosen much faster. Experiment results show that the inter-cluster distance 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. © 2008 Elsevier Ltd. All rights reserved. |
URI: | http://scholars.lib.ntu.edu.tw/handle/123456789/352571 https://www.scopus.com/inward/record.uri?eid=2-s2.0-58249083168&doi=10.1016%2fj.patcog.2008.08.030&partnerID=40&md5=4bb1ee7757d9204b3146a9334adfcd61 |
ISSN: | 00313203 | DOI: | 10.1016/j.patcog.2008.08.030 | SDG/關鍵字: | Classifiers; Image retrieval; Learning systems; Vectors; Computation times; Feature spaces; Grid searches; Inter-cluster distances; Kernel parameters; Penalty parameters; SVM; Svm classifiers; Testing accuracies; Testing processes; Training processes; Training times; Support vector machines |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。