Abstract
In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well as emerging learning techniques such as Regularization Networks and Support Vector Machines which can be justified in term of the same induction principle.
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Evgeniou, T., Pontil, M. & Poggio, T. Statistical Learning Theory: A Primer. International Journal of Computer Vision 38, 9–13 (2000). https://doi.org/10.1023/A:1008110632619
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DOI: https://doi.org/10.1023/A:1008110632619