Terascale simulations produce data that is vast in spatial, temporal, and variable domains, creating a formidable challenge for subsequent analysis. Feature extraction as a data r...
The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in mac...
In this paper, a novel training method is proposed to increase the classification efficiency of support vector machine (SVM). The efficiency of the SVM is determined by the number ...
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Atomicity (or linearizability) is a commonly used consistency criterion for distributed services and objects. Although atomic object implementations are abundant, proving that algo...
Gregory Chockler, Nancy A. Lynch, Sayan Mitra, Jos...