In this paper we analyze the PAC learning abilities of several simple iterative algorithms for learning linear threshold functions, obtaining both positive and negative results. W...
We explore combining reinforcement learning with a hand-crafted local controller in a manner suggested by the chaotic control algorithm of Vincent, Schmitt and Vincent (1994). A c...
The advances in kernel-based learning necessitate the study on solving a large-scale non-sparse positive definite linear system. To provide a deterministic approach, recent resear...
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
Although Programming by Demonstration (PBD) has the potential to improve the productivity of unsophisticated users, previous PBD systems have used brittle, heuristic, domain-speci...