The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a n...
In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attem...
Chun Chen, Zhengguang Chen, Jiajun Bu, Can Wang, L...
A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
Abstract. In analyzing diffusion magnetic resonance imaging, multitensor models address the limitations of the single diffusion tensor in situations of partial voluming and fiber c...
Thomas Schultz, Carl-Fredrik Westin, Gordon L. Kin...
Stream applications gained significant popularity over the last years that lead to the development of specialized stream engines. These systems are designed from scratch with a di...