This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
In this paper we propose an ontology (formal knowledge base) creation methodology based on integrating external ontologies into the one developed by a community of the domain expe...
The focus of my thesis is on the development of a multi-method framework for the validation of formal models (domain model, user model, and teaching model) for adaptive work-integr...
Most video retrieval systems are multimodal, commonly relying on textual information, low- and high-level semantic features extracted from query visual examples. In this work, we ...