We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
Executing critical systems often rely on humans to make important and sometimes life-critical decisions. As such systems become more complex, the potential for human error to lead...
Lori A. Clarke, Leon J. Osterweil, George S. Avrun...
Abstract. A statistical technique is developed for estimating the support of itemsets on data streams, regardless of the size of the data stored. This technique, which is computati...
Pierre-Alain Laur, Jean-Emile Symphor, Richard Noc...
Researchers have recognized the need for more expressive descriptions of Web services. Most approaches have suggested using ontologies to either describe the Web services or to an...
Nicole Oldham, Christopher Thomas, Amit P. Sheth, ...
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...