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NIPS
2007
15 years 4 months ago
Bayesian Co-Training
We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumption...
Shipeng Yu, Balaji Krishnapuram, Rómer Rosa...
138
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AAAI
1998
15 years 4 months ago
Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model
One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding o...
Abigail S. Gertner, Cristina Conati, Kurt VanLehn
95
Voted
NIPS
1998
15 years 3 months ago
Efficient Bayesian Parameter Estimation in Large Discrete Domains
In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discreteoutcomes,most of which do not appearin the training ...
Nir Friedman, Yoram Singer
147
Voted
UAI
2007
15 years 3 months ago
User-Centered Methods for Rapid Creation and Validation of Bayesian Belief Networks
Bayesian networks (BN) are particularly well suited to capturing vague and uncertain knowledge. However, the capture of this knowledge and associated reasoning from human domain e...
Jonathan D. Pfautz, Zach Cox, Geoffrey Catto, Davi...
104
Voted
CORR
2006
Springer
154views Education» more  CORR 2006»
15 years 2 months ago
Functional Bregman Divergence and Bayesian Estimation of Distributions
Abstract--A class of distortions termed functional Bregman divergences is defined, which includes squared error and relative entropy. A functional Bregman divergence acts on functi...
B. A. Frigyik, Santosh Srivastava, Maya R. Gupta