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» Structure learning of Bayesian networks using constraints
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ICMLA
2010
15 years 2 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
ECTEL
2007
Springer
15 years 10 months ago
Scruffy Technologies to Enable (Work-integrated) Learning
Abstract. The goal of the APOSDLE (Advanced Process-Oriented SelfDirected Learning environment) project is to support work-integrated learning of knowledge workers. We argue that w...
Stefanie N. Lindstaedt, Peter Scheir, Armin Ulbric...
IMC
2006
ACM
15 years 10 months ago
Towards IP geolocation using delay and topology measurements
We present Topology-based Geolocation (TBG), a novel approach to estimating the geographic location of arbitrary Internet hosts. We motivate our work by showing that 1) existing a...
Ethan Katz-Bassett, John P. John, Arvind Krishnamu...
TNN
2008
178views more  TNN 2008»
15 years 4 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen
JAIR
2002
120views more  JAIR 2002»
15 years 4 months ago
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
Hagit Shatkay, Leslie Pack Kaelbling