This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
This paper promotes the use of supervised machine learning in laboratory settings where chemists have a large number of samples to test for some property, and are interested in id...
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
In this paper, we develop techniques based on evolvability statistics of the fitness landscape surrounding sampled solutions. Averaging the measures over a sample of equal fitness...
Tom Smith, Phil Husbands, Paul J. Layzell, Michael...
Abstract. The optimal allocation of samples for activity-level detection in a wireless body area network for health-monitoring applications is considered. A wireless body area netw...
Gautam Thatte, Viktor Rozgic, Ming Li, Sabyasachi ...