In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
This paper presents a new algorithm for video-object segmentation, which combines motion-based segmentation, high-level object-model detection, and spatial segmentation into a sin...
Dirk Farin, Peter H. N. de With, Wolfgang Effelsbe...
Theorem proving techniques are particularly well suited for reasoning about arithmetic above the bit level and for relating di erent f abstraction. In this paper we show how a non-...
John W. O'Leary, Miriam Leeser, Jason Hickey, Mark...
This paper describes the participation of Idiap-MULTI to the Robot Vision Task at imageCLEF 2010. Our approach was based on a discriminative classification algorithm using multiple...