Improved scheme to accelerate support vector regression

摘要

The computational cost of support vector regression in the training phase is O(N^3),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.