Abstract. We describe a probabilistic reference disambiguation mechanism developed for a spoken dialogue system mounted on an autonomous robotic agent. Our mechanism performs proba...
Many applications require the ability to identify data that is anomalous with respect to a target group of observations, in the sense of belonging to a new, previously unseen `atta...
Abstract. Graph-based representations have been used with considercess in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the ...
Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learne...
The task of finding the optimum of some function f(x) is commonly accomplished by generating and testing sample solutions iteratively, choosing each new sample x heuristically on t...
Abstract. Bias variance decomposition for classifiers is a useful tool in understanding classifier behavior. Unfortunately, the literature does not provide consistent guidelines on...
This paper presents a logic-based bargaining solution based on Zhang and Zhang's framework. It is shown that if the demand sets of players are logically closed, the solution s...
The concept of Interchangeability was developed to deal with redundancy of values in the same domain. Conventional algorithms for detecting Neighborhood Interchangeability work by ...
This paper introduces the controlled natural language PENG Light together with a language processor that is based on a bidirectional grammar. The language processor has the followi...
Hidden Markov Models are a widely used generative model for analysing sequence data. A variant, Profile Hidden Markov Models are a special case used in Bioinformatics to represent,...
Stefan Mutter, Bernhard Pfahringer, Geoffrey Holme...