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» Decision Making Using Probabilistic Inference Methods
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AAAI
2011
13 years 9 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
AAAI
2008
14 years 12 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, ...
EUSFLAT
2003
117views Fuzzy Logic» more  EUSFLAT 2003»
14 years 11 months ago
On a modeling of decision making with a twofold integral
A Sugeno and a Choquet integrals are commonly used fuzzy integrals for aggregation. As a generalization of both integrals, the twofold integral is induced. The twofold integral en...
Hideyuki Imai, Vicenç Torra
GLOBECOM
2010
IEEE
14 years 7 months ago
Cognitive Network Inference through Bayesian Network Analysis
Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios ...
Giorgio Quer, Hemanth Meenakshisundaram, Tamma Bhe...
ICB
2007
Springer
161views Biometrics» more  ICB 2007»
15 years 1 months ago
Latent Identity Variables: Biometric Matching Without Explicit Identity Estimation
Abstract. We present a new approach to biometrics that makes probabilistic inferences about matching without ever estimating an identity "template". The biometric data is...
Simon J. D. Prince, Jania Aghajanian, Umar Mohamme...