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» Modeling Classification and Inference Learning
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AAAI
2008
15 years 7 months ago
Maximum Entropy Inverse Reinforcement Learning
Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recoveri...
Brian Ziebart, Andrew L. Maas, J. Andrew Bagnell, ...
UAI
1998
15 years 6 months ago
Learning From What You Don't Observe
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algor...
Mark A. Peot, Ross D. Shachter
163
Voted
MICCAI
2010
Springer
15 years 3 months ago
Incremental Shape Statistics Learning for Prostate Tracking in TRUS
Abstract. Automatic delineation of the prostate boundary in transrectal ultrasound (TRUS) can play a key role in image-guided prostate intervention. However, it is a very challengi...
Pingkun Yan, Jochen Kruecker
175
Voted
JMLR
2010
137views more  JMLR 2010»
14 years 12 months ago
Covariance in Unsupervised Learning of Probabilistic Grammars
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while...
Shay B. Cohen, Noah A. Smith
167
Voted
4OR
2006
118views more  4OR 2006»
15 years 5 months ago
Dealing with inconsistent judgments in multiple criteria sorting models
Sorting models consist in assigning alternatives evaluated on several criteria to ordered categories. To implement such models it is necessary to set the values of the preference p...
Vincent Mousseau, Luis C. Dias, José Rui Fi...