Abstract
This paper presents a method to iteratively grow a compact Support Vector Regressor so that the balance between size of the machine and its performance can be user-controlled. The algorithm is able to combine Gaussian kernels with different spread parameter, skipping the ‘a priori’ parameter estimation by allowing a progressive incorporation of nodes with decreasing values of the spread parameter, until a cross-validation stopping criterion is met. Experimental results show the significant reduction achieved in the size of the machines trained with this new algorithm and their good generalization capabilities.
This work has been partially supported by Spanish Government CICYT grant TIC2002-03713.
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Gutiérrez-González, D., Parrado-Hernández, E., Navia-Vázquez, A. (2005). Multi-kernel Growing Support Vector Regressor. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_44
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DOI: https://doi.org/10.1007/11494669_44
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