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In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) methodology. We apply a fuzzy membership to each input point and reformulate the optimization problem of SVM such that different input points can make different contributions to the learning of decision surface. Besides, the parameters to be identified in the SVM, such as the components within the weight...
In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 les v les 1 on controlling...
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