Advances in network technology continue to improve the communication performance of workstation and PC clusters, making high-performance workstation-clustercomputing increasingly ...
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
Most algorithms for solving Markov decision processes rely on a discount factor, which ensures their convergence. It is generally assumed that using an artificially low discount f...
Two methods are described for enhancing performance of branch and bound methods for overconstrained CSPS. These methods improve either the upper or lower bound, respectively, duri...
In this paper, we propose an Active Learning (AL) framework for the Multi-Task Adaptive Filtering (MTAF) problem. Specifically, we explore AL approaches to rapidly improve an MTAF...