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» Extremal Problems of Information Combining
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AVSS
2006
IEEE
14 years 1 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
FM
2006
Springer
133views Formal Methods» more  FM 2006»
14 years 1 months ago
Enforcer - Efficient Failure Injection
Non-determinism of the thread schedule is a well-known problem in concurrent programming. However, other sources of non-determinism exist which cannot be controlled by an applicati...
Cyrille Artho, Armin Biere, Shinichi Honiden
EDM
2010
160views Data Mining» more  EDM 2010»
13 years 11 months ago
Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System
Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distr...
Jon M. Fincham, John R. Anderson, Shawn Betts, Jen...
SDM
2007
SIAM
169views Data Mining» more  SDM 2007»
13 years 11 months ago
Rank Aggregation for Similar Items
The problem of combining the ranked preferences of many experts is an old and surprisingly deep problem that has gained renewed importance in many machine learning, data mining, a...
D. Sculley
JAIR
2006
160views more  JAIR 2006»
13 years 9 months ago
Anytime Point-Based Approximations for Large POMDPs
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun