Sparse regression is the problem of selecting a parsimonious subset of all available regressors for an efficient prediction of a target variable. We consider a general setting in w...
Traditional regression analysis derives global relationships between variables and neglects spatial variations in variables. Hence they lack the ability to systematically discover...
We combine the replica approach from statistical physics with a variational approach to analyze learning curves analytically. We apply the method to Gaussian process regression. A...
We introduce a semi-supervised learning estimator which tends to the first kernel principal component as the number of labeled points vanishes. We show application of the proposed...
Leonardo Angelini, Daniele Marinazzo, Mario Pellic...
Fitting statistical models is a widely employed technique for the segmentation of medical images. While this approach gives impressive results for simple structures, shape models a...