Multikernel semiparametric linear programming support vector regression

In many real life realms, many unknown systems own different data trends in different regions, i.e., some parts are steep variations while other parts are smooth variations. If we utilize the conventional kernel learning algorithm, viz. the single kernel linear programming support vector regression, to identify these systems, the identification results are usually not very good. Hence, we exploit the nonlinear mappings induced from the kernel functions as the admissible functions to construct a novel multikernel semiparametric predictor, called as MSLP-SVR, to improve the regression effectiveness. The experimental results on the synthetic and the real-world data sets corroborate the efficacy and validity of our proposed MSLP-SVR. Meantime, compared with other multikernel linear programming support vector algorithm, ours also takes advantages. In addition, although the MSLP-SVR is proposed in the regression domain, it can also be extended to classification problems.