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Support vector machines (SVMs) have been successfully applied to classification problems. Practical issues Involve how to determine the right type and suitable hyperparameters of kernel functions. Recently, multiple-kernel learning (MKL) algorithms are developed to handle these issues by combining different kernels. The weight with each kernel in the combination is obtained through learning. One of...
Support vector clustering (SVC) has been successfully applied to solve multi-class classification problems. However, it is usually hard to determine the hyper-parameters of RBF kernel functions. A multiple kernel learning (MKL) algorithm is developed to solve this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously obtained with semidefinite programming. However,...
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