We investigate why discretization is effective in naive-Bayes learning. We prove a theorem that identifies particular conditions under which discretization will result in naiveBay...
We describe a practical method for reasoning about realistic concurrent programs. Our method allows global two-state invariants that restrict update of shared state. We provide sim...
Ernie Cohen, Michal Moskal, Wolfram Schulte, Steph...
An efficient framework is proposed for the fast recovery of Bayesian network classifier. A novel algorithm, called Iterative Parent-Child learningBayesian Network Classifier (IPC-...
Computer simulation is an appealing approach for the reliability analysis of structure-based software systems as it can accommodate important complexities present in realistic sys...
We investigate to what extent flooding and routing is possible if the graph is allowed to change unpredictably at each time step. We study what minimal requirements are necessary...