Rule extraction based on support and prototype vectors
Haydemar Núñez, Cecilio Angulo and Andreu Català
In: Rule Extraction from Support Vector Machines Studies in Computational Intelligence , 80 (80). (2008) Springer , pp. 109-134. ISBN 978-3-540-75389-6

Abstract

The support vector machine (SVM) is a modelling technique based on the statistical learning theory, 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.

[Edit]