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AAAI
2008
13 years 11 months ago
Bounding the False Discovery Rate in Local Bayesian Network Learning
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...
Ioannis Tsamardinos, Laura E. Brown
RIA
2006
84views more  RIA 2006»
13 years 8 months ago
Learning Recursive Automata from Positive Examples
In this theoretical paper, we compare the "classical" learning techniques used to infer regular grammars from positive examples with the ones used to infer categorial gra...
Isabelle Tellier
AAAI
1996
13 years 10 months ago
Learning Models of Intelligent Agents
Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interactive strategy is a ha...
David Carmel, Shaul Markovitch
ADCM
2010
59views more  ADCM 2010»
13 years 9 months ago
Sampling inequalities for infinitely smooth functions, with applications to interpolation and machine learning
Sampling inequalities give a precise formulation of the fact that a differentiable function cannot attain large values, if its derivatives are bounded and if it is small on a suff...
Christian Rieger, Barbara Zwicknagl
JMLR
2008
79views more  JMLR 2008»
13 years 8 months ago
Manifold Learning: The Price of Normalization
We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consis...
Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Rit...