Statistical Learning Theory: A Primer

Authors:
Theodoros Evgeniou;Massimiliano Pontil;Tomaso Poggio
Affiliations:
Center for Biological and Computational Learning, Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. theos@ai.mit.edu;Center for Biological and Computational Learning, Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. pontil@ai.mit.edu;Center for Biological and Computational Learning, Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. tp@ai.mit.edu
Venue:
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Year:
2000

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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.