This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of e...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
Abstract--The ranking problem has become increasingly important in modern applications of statistical methods in automated decision making systems. In particular, we consider a for...
Abstract. We consider two natural generalizations of the notion of transversal to a finite hypergraph, arising in data-mining and machine learning, the so called multiple and parti...
Endre Boros, Vladimir Gurvich, Leonid Khachiyan, K...
Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. B...
In the traditional setting, text categorization is formulated as a concept learning problem where each instance is a single isolated document. However, this perspective is not appr...