Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
Abstract. We consider the problem of learning a mapping from question to answer messages. The training data for this problem consist of pairs of messages that have been received an...
Abstract. Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. There exist several convergent and consistent RL algorithms which have been intensivel...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...
Finding multiple occurrences of themes and patterns in music can be hampered due to polyphonic textures. This is caused by the complexity of music that weaves multiple independent...
Abstract. We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but r...