Support vector machines (SVMs) have proved to be promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient properties that other methods hardly provide. The critical SVMs problem is to select appropriate kernels as the performance of SVMs depends on this choice. In this paper, a set of new Hermite kernel functions is ...
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