We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
With multimedia applications, the Internet traffic increased a lot in volume, but also suffered major alterations in its nature, requesting other network services besides the curr...
The automated detection and tracking of humans in computer vision necessitates improved modeling of the human skin appearance. In this paper we propose a Bayesian network approach...
Ira Cohen, Nicu Sebe, Theo Gevers, Thomas S. Huang
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...