Rule extraction based on support and prototype vectors

Abstract:

The support vector machine (SVM) is a modelling technique based on the statistical learning theory (Cortes and Vapnik 1995; Cristianini and Shawe-Taylor 2000; Vapnik 1998), which has been successfully applied initially in classification problems and later extended in different domains to other kind of problems like regression or novel detection. As a learning tool, it has demonstrated its strength especially in the cases where a data set of reduced size is at hands and/or when input space is of a high dimensionality. Nevertheless, a possible limitation of the SVMs is, similarly to the neuronal networks case, that they are only able of generating results in the form of black box models; that is, the solution provided by them is difficult to be interpreted from the point of view of the user. © 2008 Springer-Verlag Berlin Heidelberg.

Año de publicación:

2008

Keywords:

Fuente:

scopus