Network data models are frequently used as a mechanism to describe the connectivity between spatial features in many emerging GIS applications (location-based services, transporta...
Petko Bakalov, Erik G. Hoel, Wee-Liang Heng, Vassi...
Finding data items is one of the most basic services of any distributed system. It is particular challenging in ad-hoc networks, due to their inherent decentralized nature and lac...
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an...
Modern society is highly dependent on the smooth and safe flow of information over communication and computer networks. Computer viruses and worms pose serious threats to the soci...