Kazuki Iwamura

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— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky factorization of the kernel matrix, and train the SVM in the dual form in the reduced empirical feature space. Since the mapped linearly independent training data span the(More)
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