This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes ...
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
We present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of p...
In this paper we present a technique for automatically generating constraints on parameter derivatives that reduce ambiguity in the behaviour prediction. Starting with a behaviour...
We study learning scenarios in which multiple learners are involved and “nature” imposes some constraints that force the predictions of these learners to behave coherently. Thi...