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ATAL
2007
Springer
15 years 8 months ago
Sharing experiences to learn user characteristics in dynamic environments with sparse data
This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agen...
David Sarne, Barbara J. Grosz
BMCBI
2007
98views more  BMCBI 2007»
15 years 1 months ago
Duration learning for analysis of nanopore ionic current blockades
Background: Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties, with potential implications ...
Alexander G. Churbanov, Carl Baribault, Stephen Wi...
ICIP
2008
IEEE
16 years 3 months ago
A model for image patch-based algorithms
An empirical study of the domain of patch-based learning algorithms for image and video processing is conducted. As patch-based algorithms are commonly used, knowledge of the prop...
Karl S. Ni, Truong Q. Nguyen
NIPS
2003
15 years 3 months ago
Probabilistic Inference in Human Sensorimotor Processing
When we learn a new motor skill, we have to contend with both the variability inherent in our sensors and the task. The sensory uncertainty can be reduced by using information abo...
Konrad P. Körding, Daniel M. Wolpert
ICML
2010
IEEE
15 years 2 months ago
Modeling Interaction via the Principle of Maximum Causal Entropy
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
Brian Ziebart, J. Andrew Bagnell, Anind K. Dey