Machine learning typically involves discovering regularities in a training set, then applying these learned regularities to classify objects in a test set. In this paper we presen...
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...
Many works have shown that strong connections relate learning from examples to regularization techniques for ill-posed inverse problems. Nevertheless by now there was no formal ev...
Lorenzo Rosasco, Andrea Caponnetto, Ernesto De Vit...
A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, th...
In the paper we propose a new type of regularization procedure for training sparse Bayesian methods for classification. Transforming Hessian matrix of log-likelihood function to d...