This paper investigates basic research issues that need to be addressed in order to reuse learning objects in a flexible way. We propose an ontology based approach. Our ontology f...
Katrien Verbert, Joris Klerkx, Michael Meire, Jeha...
This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
We present a pointwise approach to Japanese morphological analysis (MA) that ignores structure information during learning and tagging. Despite the lack of structure, it is able t...
Abstract. This paper examines the generalization capability in learning multiple temporal patterns by the recurrent neural network with parametric bias (RNNPB). Our simulation expe...