Nir Friedman, Stuart J. Russell
"Background subtraction" is an old technique for finding moving objects in a video sequence---for example, cars driving on a freeway. The idea is that subtracting the current image from a time-averaged background image will leave ...
David Maxwell Chickering, David Heckerman, Christopher Meek
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has ...
Maite López-Sánchez, Ramon López de Mántaras, Carles Sierra
Eric Horvitz, Jerome Edward Lengyel
We describe work to control graphics rendering under limited computational resources by taking a decision-theoretic perspective on perceptual costs and computational savings of approximations. The work extends earlier work on the control of rendering ...
Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of ...
Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
Assignment methods are at the heart of many algorithms for unsupervised learning and clustering --- in particular, the well-known K-means and Expectation-Maximization (EM) algorithms. In this work, we study several different methods of assignment, ...
A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly ...
Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang
Most exact algorithms for general partially observable Markov decision processes (pomdps) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine ...
Nir Friedman, Moisés Goldszmidt
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new ...
Kathryn Blackmond Laskey, Suzanne M. Mahoney