Takamasa Sekine

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In this paper, we propose a novel method of sparse least squares support vector machine (SLS-SVM) that is trained in each class empirical feature space spanned by the independent training data belonging to the associated class. And we determine the decision function in each class empirical feature space. To prevent that the information of other classes is(More)
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