Regularization and statistical learning theory for data analysis
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.
statistical learning theory regularization theory support vector machine regularization networks image analysis applications