We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data. This side effect provides the opportunity for innovative meth...
Anna Monreale, Fabio Pinelli, Roberto Trasarti, Fo...
We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news ar...
B. Delaney, Andrew S. Fast, W. M. Campbell, C. J. ...
Abstract Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic managemen...
Machine learning approaches offer some of the most cost-effective approaches to building predictive models (e.g., classifiers) in a broad range of applications in computational bio...