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» Learning Generative Models with the Up-Propagation Algorithm
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ICGI
2010
Springer
13 years 6 months ago
Learning PDFA with Asynchronous Transitions
In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time length...
Borja Balle, Jorge Castro, Ricard Gavaldà
JMLR
2010
162views more  JMLR 2010»
13 years 2 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...
ASC
2008
13 years 7 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
JAIR
2010
111views more  JAIR 2010»
13 years 6 months ago
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system t...
David L. Chen, Joohyun Kim, Raymond J. Mooney
GECCO
2006
Springer
138views Optimization» more  GECCO 2006»
13 years 11 months ago
Does overfitting affect performance in estimation of distribution algorithms
Estimation of Distribution Algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used t...
Hao Wu, Jonathan L. Shapiro