Abstract:In order to improve the performance of support vector regression machine

a novel algorithm was proposed to solve the support vector regression problem with multiple kernel leaning method (NS-MKR).Lp-norm constraints were imposed on kernel weights with p>1to obtain the non-sparse solution.Two-step optimization method was used to solve the modal.Firstly

standard support vector regression based on a weighted mixture kernel was optimized to obtain Lagrange multipliers.Secondly

kernel weights were solved only with some simple calculation.These two steps were executed alternately until some predefined criterions were satisfied.Experiments on artificial datasets and real datasets showed that the proposed algorithm has better performance than the existing ones which based on single kernel or sparse multiple kernel leaning method.