We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based ...
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisatio...
Abstract. We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization (GRLVQ). By introducing a full matrix of relevance factors in the dis...
Managing uncertain data using probabilistic frameworks has attracted much interest lately in the database literature, and a central computational challenge is probabilistic infere...
We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a ...