In this paper we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a ...
We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical repres...
This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes ...
In this paper, a novel background model on spatio-temporal patches is introduced for video surveillance, especially for night outdoor scene, where extreme lighting conditions ofte...
We present a fully probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree i...
Edward Meeds, David A. Ross, Richard S. Zemel, Sam...