This paper proposes a novel hypersphere support vector machines （HSVMs） based on generalized multiplicative updates. This algorithm can obtain the boundary of hypersphere containing one class of samples by the descr...This paper proposes a novel hypersphere support vector machines （HSVMs） based on generalized multiplicative updates. This algorithm can obtain the boundary of hypersphere containing one class of samples by the description of the training samples from one class and use this boundary to classify the test samples. The generalized multiplicative updates are applied to solving boundary optimization progranmning. Multiplicative updates available are suited for nonnegative quadratic convex programming. The generalized multiplicative updates are derived to box and sum constrained quadratic programming in this paper. They provide an extremely straightforward way to implement support vector machines （SVMs） where all variables are updated in parallel. The generalized multiplicative updates converge monotonically to the solution of the maximum margin hyperplane. The experiments show the superiority of our new algorithm.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No.60574075)
文摘This paper proposes a novel hypersphere support vector machines （HSVMs） based on generalized multiplicative updates. This algorithm can obtain the boundary of hypersphere containing one class of samples by the description of the training samples from one class and use this boundary to classify the test samples. The generalized multiplicative updates are applied to solving boundary optimization progranmning. Multiplicative updates available are suited for nonnegative quadratic convex programming. The generalized multiplicative updates are derived to box and sum constrained quadratic programming in this paper. They provide an extremely straightforward way to implement support vector machines （SVMs） where all variables are updated in parallel. The generalized multiplicative updates converge monotonically to the solution of the maximum margin hyperplane. The experiments show the superiority of our new algorithm.
