The nature of statistical learning theory
The nature of statistical learning theory
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Regularized kriging: the support vectors method applied to kriging
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Partially linear support vector machines applied to the prediction of mine slope movements
Mathematical and Computer Modelling: An International Journal
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In this paper we propose a simple and intuitive method for constructing partially linear models and, in general, partially parametric models, using support vector machines for regression and, in particular, using regularization networks (splines). The results are more satisfactory than those for classical nonparametric approaches. The method is based on a suitable approach to selecting the kernel by relaying on the properties of positive definite functions. No modification is required of the standard SVM algorithms, and the approach is valid for the ε-insensitive loss. The approach described here can be immediately applied to SVMs for classification and to other methods that use the kernel as the inner product.