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» Ontological Framework for Approximation
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121
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UAI
1996
15 years 2 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
109
Voted
COLING
1992
15 years 1 months ago
Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora
This paper describes a program that disambignates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's ca...
David Yarowsky
93
Voted
NIPS
1992
15 years 1 months ago
Some Solutions to the Missing Feature Problem in Vision
In visual processing the ability to deal with missing and noisy information is crucial. Occlusions and unreliable feature detectors often lead to situations where little or no dir...
Subutai Ahmad, Volker Tresp
ATAL
2010
Springer
15 years 1 months ago
Risk-sensitive planning in partially observable environments
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
Janusz Marecki, Pradeep Varakantham
APN
2010
Springer
15 years 29 days ago
Forward Analysis for Petri Nets with Name Creation
Pure names are identifiers with no relation between them, except equality and inequality. In previous works we have extended P/T nets with the capability of creating and managing p...
Fernando Rosa Velardo, David de Frutos-Escrig