Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization ...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
Rodolphe Jenatton, Guillaume Obozinski, Francis Ba...
A Bayesian network model is a popular technique for data mining due to its intuitive interpretation. This paper presents a semantic genetic algorithm (SGA) to learn a complete qual...
Abstract— Analyzing unknown data sets such as multispectral images often requires unsupervised techniques. Data clustering is a well known and widely used approach in such cases....
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conve...