Multiple Kernel Linear Programming Support Vector Regression Incorporating Prior Knowledge
集成先验知识的多核线性规划支持向量回归

In order to obtain an accurate model from a small data set, a novel multiple kernel linear programming support vector regression with prior knowledge is presented in this paper. The algorithm firstly incorporates the data that is possibly biased from the prior simulator into the existing linear programming support vector regression by modifying optimization objectives and inequality constraints. Then, multiple kernels are introduced to integrate the linear programming support vector regression with prior knowledge, in order to achieve an accurate modeling for complex and changeful problems. Finally, the algorithm has also been generalized to model the multi-input multi-output data. The synthetic example and practical applications of an antenna and a cavity filter show that the proposed algorithm is simple, and that the obtained model is sparse and accurate.