Sparse multikernel support vector regression machines trained by active learning

A method for the sparse multikernel support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on its influence on the accuracy of the model using the active learning principle. A different kernel function is attributed to each training data point, yielding multikernel regressor. The advantages of the proposed method are illustrated on several examples and the experiments show the advantages of the proposed method.

► We present multikernel SVR trained by adapting the active learning principle. ► The proposed algorithm achieves high accuracy while keeping the complexity low. ► The complexity is reduced by keeping the number of support vectors low.