Abstract. We propose a machine learning approach to action prediction in oneshot games. In contrast to the huge literature on learning in games where an agent's model is deduc...
Abstract. This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an associat...
Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inabili...
Increasingly scientists are using collections of software tools in their research. These tools are typically used in concert, often necessitating laborious and error prone manual ...
We describe a Markov chain Bayesian classification tool, SCS, that can perform data-driven classification of proteins and protein segments. Training data for interesting classific...
Timothy Meekhof, Gary W. Daughdrill, Robert B. Hec...