کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532714 869986 2009 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 42, Issue 5, May 2009, Pages 710–717
نویسندگان
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