Abstract
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.
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Matías, J.M. (2005). Partially Parametric SVM. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_7
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DOI: https://doi.org/10.1007/11595014_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
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