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Fuzzy support vector machine (FSVM) have been very successful in pattern recognition problems with outliers or noises. FSVM enhances the SVM in reducing the effect of noises in data points. In this paper, we introduce FSVM to regression problems for function approximation with noises. We apply a fuzzy membership to each input point of SVR and reformulate SVR into fuzzy SVR (FSVR) such that different...
In function approximation, if datasets have many redundant input variables, various problems such as deterioration of the generalization ability and an increase of the computational cost may occur. One of the methods to solve these problems is variable selection. In pattern recognition, the effectiveness of backward variable selection by block deletion is shown. In this paper, we extend this method...
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