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NN
2006
Springer
127views Neural Networks» more  NN 2006»
15 years 6 months ago
The asymptotic equipartition property in reinforcement learning and its relation to return maximization
We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical seque...
Kazunori Iwata, Kazushi Ikeda, Hideaki Sakai
NN
2006
Springer
141views Neural Networks» more  NN 2006»
15 years 6 months ago
Encoding uncertainty in the hippocampus
The medial temporal lobe may play a critical role in binding successive events into memory while encoding contextual information in implicit and explicit memory tasks. Information...
Lee M. Harrison, Andrew Duggins, Karl J. Friston
ECAI
2010
Springer
15 years 3 months ago
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space
The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket ...
Qiang Lou, Zoran Obradovic
JMLR
2010
140views more  JMLR 2010»
15 years 1 months ago
Learning Non-Stationary Dynamic Bayesian Networks
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Joshua W. Robinson, Alexander J. Hartemink
ICASSP
2011
IEEE
14 years 10 months ago
Powerful extensions to CRFS for grapheme to phoneme conversion
Conditional Random Fields (CRFs) have proven to perform well on natural language processing tasks like name transliteration, concept tagging or grapheme-to-phoneme (g2p) conversio...
Stefan Hahn, Patrick Lehnen, Hermann Ney