Regularization and statistical learning theory for data analysis

Authors:
Theodoros Evgeniou;Tomaso Poggio;Massimiliano Pontil;Alessandro Verri
Affiliations:
Technology Management, INSEAD, Fointainebleau 77305, France;Center for Biological and Computational Learning, MIT, 45 Carleton St., Cambridge, MA;Center for Biological and Computational Learning, MIT, 45 Carleton St., Cambridge, MA;INFM-DISI, Università di Genova, Via Dodecaneso 35, 16146 Genova, Italy
Venue:
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Year:
2002

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Abstract

Problems of data analysis, like classification and regression, can be studied in the framework of Regularization Theory as ill-posed problems, or through Statistical Learning Theory in the learning-from-example paradigm. In this paper we highlight the connections between these two approaches and discuss techniques, like support vector machines and regularization networks, which can be justified in this theoretical framework and proved to be useful in a number of image analysis applications.