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- W2398101415 abstract "The computational cost of support vector regression in the training phase is O (N3), which is very expensive for a large scale problem. In addition, the solution of support vector regression is of parsimoniousness, which has relation to a part of the whole training data set. Hence, it is reasonable to reduce the training data set. Aiming at the scheme based on k-nearest neighbors to reduce the training data set with the computational complexity O (kMN^2), an improved scheme is proposed to accelerate the reducing phase, which cuts down the computational complexity from O (kMN^2) to O (MN^2). Finally, experimental results on benchmark data sets validate the effectiveness of the improved scheme." @default.
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- W2398101415 date "2009-10-30" @default.
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- W2398101415 title "Improved scheme to accelerate support vector regression" @default.
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