We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approxima...
We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the...
Abstract. We introduce an ontology-based semantic modelling framework that addresses subject domain modelling, instruction modelling, and interoperability aspects in the developmen...
Abstract. Given the huge amount of static and dynamic content created for eLearning tasks, the major challenge for extending their use is to improve the effectiveness of retrieval ...
The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by com...
Neil D. Lawrence, John C. Platt, Michael I. Jordan