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» A Bayesian Approach to Semi-Supervised Learning
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AAAI
2010
13 years 10 months ago
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
SEMWEB
2004
Springer
14 years 2 months ago
METEOR-S Web Service Annotation Framework with Machine Learning Classification
Researchers have recognized the need for more expressive descriptions of Web services. Most approaches have suggested using ontologies to either describe the Web services or to an...
Nicole Oldham, Christopher Thomas, Amit P. Sheth, ...
ACL
2008
13 years 10 months ago
Learning Document-Level Semantic Properties from Free-Text Annotations
This paper demonstrates a new method for leveraging unstructured annotations to infer semantic document properties. We consider the domain of product reviews, which are often anno...
S. R. K. Branavan, Harr Chen, Jacob Eisenstein, Re...
ECML
2001
Springer
14 years 1 months ago
Comparing the Bayes and Typicalness Frameworks
When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these...
Thomas Melluish, Craig Saunders, Ilia Nouretdinov,...
ICML
2004
IEEE
14 years 9 months ago
Large margin hierarchical classification
We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...
Ofer Dekel, Joseph Keshet, Yoram Singer