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JOCN
2011
78views more  JOCN 2011»
13 years 2 months ago
Changes in Cerebello-motor Connectivity during Procedural Learning by Actual Execution and Observation
■ The cerebellum is involved in motor learning of new procedures both during actual execution of a motor task and during observational training. These processes are thought to d...
Sara Torriero, Massimiliano Oliveri, Giacomo Koch,...
NIPS
2008
13 years 9 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
SBRN
2000
IEEE
14 years 3 days ago
Adaptation of Parameters of BP Algorithm Using Learning Automata
d Articles >> Table of Contents >> Abstract VI Brazilian Symposium on Neural Networks (SBRN'00) p. 24 Adaptation of Parameters of BP Algorithm Using Automata Hamid...
Hamid Beigy, Mohammad Reza Meybodi
ICML
2010
IEEE
13 years 8 months ago
Learning Markov Logic Networks Using Structural Motifs
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Stanley Kok, Pedro Domingos
SODA
2001
ACM
79views Algorithms» more  SODA 2001»
13 years 9 months ago
Learning Markov networks: maximum bounded tree-width graphs
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
David R. Karger, Nathan Srebro