We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
The first goal of this paper is to empirically explore the relationships between existing object-oriented coupling, cohesion, and inheritance measures and the probability of fault...
In recent years, an increasing amount of attention has been paid to information systems (IS) outsourcing by practitioners as well as academics. However, our understanding of the fa...
GPS-enabled mobile devices are a quickly growing market and users are starting to share their location information with each other through services such as Google Latitude. Locati...