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Support vector machine (SVM) is an efficient machine learning method for classification. In this paper, we propose two variable selection criteria for SVM that use wrapper methods. The criteria measur...e the contribution of each variable for a target function. The variable importance is quantified on the basis of the measured amount. The methods have high computational efficiency because they evaluate the importance of all variables without recursive calculations. They were applied to several artificial and real-world data sets, and their results were superior to those of existing methods.続きを見る
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