A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
— System-on-Chip designs often have a large number of timing domains. Communication between these domains requires synchronization, and the failure probabilities of these synchro...
—Markov chains with Labelled Transitions can be used to generate test cases in a model-based approach. These test cases are generated by random walks on the model according to pr...
Naive Bayesian classifiers have been very successful in attribute-value representations. However, it is not clear how the decomposition of the probability distributions on attribu...
This paper presents a projection algorithm for incremental control rule synthesis. The algorithm synthesizes an initial set of goal-achieving control rules using a combination of ...