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 ...
Abstract: Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about com...
Stefan Schaal, Christopher G. Atkeson, Sethu Vijay...
Peer-to-peer (P2P) networks represent an effective way to share information, since there are no central points of failure or bottleneck. However, the flip side to the distributive...
We present a framework for processing point-based surfaces via partial differential equations (PDEs). Our framework efficiently and effectively brings well-known PDE-based process...
Rapid, visual understanding of volumetric datasets is a crucial outcome of a good volume rendering application, but few current volume rendering systems deliver this result. Our g...