We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models--Hidden Markov Random Fields (H...
Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
This paper describes and contrasts findings from two related projects where groups of science pupils investigated local air pollution using a collection of mobile sensors and devic...
Abstract. Automated Text Categorization has reached the levels of accuracy of human experts. Provided that enough training data is available, it is possible to learn accurate autom...
In this paper we report on an interdisciplinary course "Computing and Art" taught at the Sabanci University, Istanbul for the first time in fall of 2004. We also present...
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...