To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random w...
This paper investigates a new approach for training discriminant classifiers when only a small set of labeled data is available together with a large set of unlabeled data. This a...
The evolution of Description Logics (DLs) and Propositional Dynamic Logics produced a hierar chy of decidable logics with multiple maximal el ements. It would be desirable to ...
Data Farming is a methodology and capability that makes use of high performance computing to run models many times. This capability gives modelers and their clients the enhanced a...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...