Evgeniou, T; Poggio, T; Pontil, M; Verri, A; (2002) Regularization and statistical learning theory for data analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS , 38 (4) 421 - 432.
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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. (C) 2002 Elsevier Science B.V. All rights reserved.
|Title:||Regularization and statistical learning theory for data analysis|
|Keywords:||statistical learning theory, regularization theory, support vector machine, regularization networks, image analysis applications|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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