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GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
16 years 1 months ago
Learning noise
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...
Michael D. Schmidt, Hod Lipson
190
Voted
CIG
2006
IEEE
16 years 1 months ago
Monte-Carlo Go Reinforcement Learning Experiments
Abstract— This paper describes experiments using reinforcement learning techniques to compute pattern urgencies used during simulations performed in a Monte-Carlo Go architecture...
Bruno Bouzy, Guillaume Chaslot
193
Voted
IEAAIE
2004
Springer
16 years 11 days ago
Machine Learning Approaches for Inducing Student Models
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...
SEMWEB
2004
Springer
16 years 10 days ago
Learning Meta-descriptions of the FOAF Network
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...
WWIC
2010
Springer
193views Communications» more  WWIC 2010»
15 years 11 months ago
0day Anomaly Detection Made Possible Thanks to Machine Learning
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...
Philippe Owezarski, Johan Mazel, Yann Labit