In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Abstract— This paper describes experiments using reinforcement learning techniques to compute pattern urgencies used during simulations performed in a Monte-Carlo Go architecture...
The main issue in e-learning is student modelling, i.e. the analysis of a student’s behaviour and prediction of his/her future behaviour and learning performance. Indeed, it is d...
Oriana Licchelli, Teresa Maria Altomare Basile, Ni...
We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontolo...
Gunnar Aastrand Grimnes, Peter Edwards, Alun D. Pr...
Abstract. This paper proposes new cognitive algorithms and mechanisms for detecting 0day attacks targeting the Internet and its communication performances and behavior. For this pu...