Previous research has addressed the scalability and availability issues associated with the construction of cluster-based network services. This paper studies the clustering of re...
Kai Shen, Tao Yang, Lingkun Chu, JoAnne Holliday, ...
This paper presents a scalable Bayesian technique for decentralized state estimation from multiple platforms in dynamic environments. As has long been recognized, centralized arch...
Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian...
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
In this paper, we present Fastrack, a parameter-free algorithm for dynamic resource provisioning that uses simple statistics to promptly distill information about changes in workl...
Andrew Caniff, Lei Lu, Ningfang Mi, Ludmila Cherka...