Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Some tasks in a dataspace (a loose collection of heterogeneous data sources) require integration of fine-grained data from diverse sources. This work is often done by end users kn...
David W. Archer, Lois M. L. Delcambre, David Maier
We present a novel method for predictive modeling of human brain states from functional neuroimaging (fMRI) data. Extending the traditional canonical correlation analysis of discre...
Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W....
When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into intere...
Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web sea...