We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
This paper describes an ongoing collaborative research program between the Computer Science and the Forestry and Wildlife Management Departments at the University of Massachusetts...
Howard J. Schultz, Dana Slaymaker, Chris Holmes, F...
Modern geographic databases can contain a large volume of data that need to be distributed to subscribed customers. The data can be modeled as a cube, where typical dimensions inc...
The present paper motivates the study of mind change complexity for learning minimal models of length-bounded logic programs. It establishes ordinal mind change complexity bounds ...
We provide algebraic semantics together with a sound and complete sequent calculus for information update due to epistemic actions. This semantics is flexible enough to accommoda...