Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
—This paper presents a semiautomatic framework that aims to produce domain concept maps from text and then to derive domain ontologies from these concept maps. This methodology p...
Abstract. The paper proposes a learning approach to support medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of Dynamic Contrast-Enhan...
Alessandro Daducci, Umberto Castellani, Marco Cris...
Abstract. We describe a new approach for learning to perform classbased segmentation using only unsegmented training examples. As in previous methods, we first use training images ...
Abstract— This paper considers the problem of learning to recognize different terrains from color imagery in a fully automatic fashion, using the robot’s mechanical sensors as ...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...