Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of them were developed while trying to simplify the quadratic programming problem that has tobe solved in training SVMs. Some others have been proposed to solve problems other than binary classification (for example one-class SVM for novelty detection).Though indeed attractive, for most of the learning machine community the above algorithms lack of a clear theoretical motivation. In this context it seems that the connection to regularization networks is most promising both from a theoretical and a practical point of view and might be of great use to understand the mathematical properties of various SV algorithms.In this paper we contribute to fill the existing gap reviewing several SV algorithms from a regularization point of view.
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