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METHOD FOR FEATURE SELECTION IN A SUPPORT VECTOR MACHINE USING FEATURE RANKING
METHOD FOR FEATURE SELECTION IN A SUPPORT VECTOR MACHINE USING FEATURE RANKING
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机译:基于特征排序的支持向量机特征选择方法
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摘要
In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score, transductive feature selection and single feature using margin-based ranking. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.
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