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» Monte Carlo Go Has a Way to Go
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ICML
2007
IEEE
14 years 7 months ago
Combining online and offline knowledge in UCT
The UCT algorithm learns a value function online using sample-based search. The TD() algorithm can learn a value function offline for the on-policy distribution. We consider three...
Sylvain Gelly, David Silver
ISBI
2004
IEEE
14 years 7 months ago
Reconstruction of 2D Pet Data with Monte Carlo Generated Natural Pixels
Abstract-In discrete detector PET, natural pixels are image basis functions calculated from responses of detector pairs. By using reconstruction with natural pixels the discretizat...
Charles L. Byrne, Stefaan Vandenberghe, Edward J. ...
SIGSOFT
2007
ACM
14 years 7 months ago
State space exploration using feedback constraint generation and Monte-Carlo sampling
The systematic exploration of the space of all the behaviours of a software system forms the basis of numerous approaches to verification. However, existing approaches face many c...
Sriram Sankaranarayanan, Richard M. Chang, Guofei ...
BMCBI
2010
105views more  BMCBI 2010»
13 years 6 months ago
The Neural/Immune Gene Ontology: clipping the Gene Ontology for neurological and immunological systems
Background: The Gene Ontology (GO) is used to describe genes and gene products from many organisms. When used for functional annotation of microarray data, GO is often slimmed by ...
Nophar Geifman, Alon Monsonego, Eitan Rubin
BMCBI
2008
170views more  BMCBI 2008»
13 years 6 months ago
Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data
Background: Researchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries o...
Tao Xu, LinFang Du, Yan Zhou