An object oriented description and framework of the Multiple ASsociative Computing (MASC) model of parallel computation is presented. This description identifies MASC objects and ...
Michael Scherger, Jerry L. Potter, Johnnie W. Bake...
We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose a selection metho...
We present a technique for defining and extracting passage-time densities from high-level stochastic process algebra models. Our high-level formalism is PEPA, a popular Markovian...
Jeremy T. Bradley, Nicholas J. Dingle, Stephen T. ...
Abstract. We introduce a new approach for tracking-based segmentation of 3D tubular structures. The approach is based on a novel combination of a 3D cylindrical intensity model and...
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...