The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
—This paper reports on experience gained and lessons learned from an intensive investigation of model-driven engineering methodology and technology for application to high-integr...
Uncertain data arises in a number of domains, including data integration and sensor networks. Top-k queries that rank results according to some user-defined score are an important...
Because of cost and resource constraints, sensor nodes do not have a complicated hardware architecture or operating system to protect program safety. Hence, the notorious buffer-o...
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the signi...
Pradeep Varakantham, Janusz Marecki, Yuichi Yabu, ...