We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machine (SVM)...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true ...
Many machine learning tasks contain feature evaluation as one of its important components. This work is concerned with attribute estimation in the problems where class distribution...
In the present paper, we study the problem of aggregation under the squared loss in the model of regression with deterministic design. We obtain sharp oracle inequalities for conve...
This paper presents a parallel framework of grid-based numerical algorithms where data dependencies between grid zones can be modeled by a directed acyclic graph (DAG). It consist...