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Comparing with traditional statistical modeling methods, support vector machine (SVM) has much advantage for solving regression and classification problems. For nonlinear regression, the kernel function of SVM transforms the nonlinear input space into a high dimensional feature space in which the solution of the problem can be represented as being a linear regression problem. Therefore, in all probability...
As is well known in statistics, the resulting linear regressors by using the rank-based Wilcoxon approach to linear regression problems are usually robust against (or insensitive to) outliers. This motivates us to introduce in this paper the Wilcoxon approach to the area of machine learning. Specifically, we investigate four new learning machines, namely Wilcoxon neural network (WNN), Wilcoxon generalized...
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