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.
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Núñez, H., Angulo, C., Català, A. (2008). Rule Extraction Based on Support and Prototype Vectors. In: Diederich, J. (eds) Rule Extraction from Support Vector Machines. Studies in Computational Intelligence, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75390-2_5
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