When evaluating the performance of SVM with two different kernel functions on a given data set, there may not be large difference in terms of classification accuracy, but there may be significant difference between two different kernels in the number of stored support vectors or training time complexity [ 33 ].
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A multi-label learning based kernel automatic recommendation method for support vector machine.
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