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ICML
2005
IEEE
16 years 7 months ago
Variational Bayesian image modelling
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
COLT
2006
Springer
15 years 10 months ago
Teaching Randomized Learners
Abstract. The present paper introduces a new model for teaching randomized learners. Our new model, though based on the classical teaching dimension model, allows to study the infl...
Frank J. Balbach, Thomas Zeugmann
ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 5 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
ACML
2009
Springer
16 years 1 months ago
Injecting Structured Data to Generative Topic Model in Enterprise Settings
Enterprises have accumulated both structured and unstructured data steadily as computing resources improve. However, previous research on enterprise data mining often treats these ...
Han Xiao, Xiaojie Wang, Chao Du
ICML
2004
IEEE
16 years 7 months ago
Utile distinction hidden Markov models
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Part...
Daan Wierstra, Marco Wiering