In this work, we proposed to use the Zoomed Ranking approach to rank and select time series models. Zoomed Ranking, originally proposed to generate a ranking of candidate algorith...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...
In this paper, a new selective sampling method for the active learning framework is presented. Initially, a small training set ? and a large unlabeled set ? are given. The goal is...
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
This paper deals with test case selection from axiomatic specifications whose axioms are quantifier-free first-order formulas with equality. We first prove the existence of an ide...