Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propo...
Abstract. Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search mis...
We describe a reinforcement learning system that transfers skills from a previously learned source task to a related target task. The system uses inductive logic programming to ana...
Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richa...
Nearest neighbor forecasting models are attractive with their simplicity and the ability to predict complex nonlinear behavior. They rely on the assumption that observations simila...
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...