Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
This paper argues that proactive behaviour, caused by high engagement and motivation of the learners, may lead to failure of collaborative learning. By examining empirical data fr...
Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive stat...
Ingmar Visser, Maartje E. J. Raijmakers, Peter C. ...
Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it i...