Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing ...
Alfredo Vellido, Elia Biganzoli, Paulo J. G. Lisbo...
We apply kernel-based machine learning methods to online learning situations, and look at the related requirement of reducing the complexity of the learnt classifier. Online meth...
Statistical machine learning methods are employed to train a Named Entity Recognizer from annotated data. Methods like Maximum Entropy and Conditional Random Fields make use of fe...
We consider the question of why modern machine learning methods like support vector machines outperform earlier nonparametric techniques like kNN. Our approach investigates the lo...
Abstract. This paper reports our comparative evaluation of three machine learning methods on Chinese text categorization. Whereas a wide range of methods have been applied to Engli...
We propose a method to recognize the `social attitude' of users towards an Embodied Conversational Agent (ECA) from a combination of linguistic and prosodic features. After de...
Fiorella de Rosis, Anton Batliner, Nicole Novielli...
Learning to predict rare events from sequences of events with categorical features is an important, real-world, problem that existing statistical and machine learning methods are ...
: Image understanding often requires extensive background knowledge. The problem addressed in this paper is such knowledge can be acquired. We discuss how relational machine learni...
Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine l...
We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the follow...