The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record ...
Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with finite Vapnik-Chervonenkis (VC) dimension. The fundamental quantity of interest i...
We present an efficient system for video search that maximizes the use of human bandwidth, while at the same time exploiting the machine’s ability to learn in real-time from use...
Alexander G. Hauptmann, Wei-Hao Lin, Rong Yan, Jun...
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...