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» Active learning in very large databases
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JCDL
2006
ACM
119views Education» more  JCDL 2006»
15 years 5 months ago
Learning from artifacts: metadata utilization analysis
Describes the MARC Content Designation Utilization Project, which is examining a very large set of metadata records as artifacts of the library cataloging enterprise. This is the ...
William E. Moen, Shawne D. Miksa, Amy Eklund, Serh...
SAFECOMP
2000
Springer
15 years 3 months ago
Expert Error: The Case of Trouble-Shooting in Electronics
An expert trouble-shooter is a subject who has a great deal of experience in his activity that allows him or her to be very efficient. However, the large amount of problems he or s...
Denis Besnard
CVPR
2008
IEEE
16 years 1 months ago
Manifold learning using robust Graph Laplacian for interactive image search
Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling a ...
Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert...
JMLR
2010
162views more  JMLR 2010»
14 years 6 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...
CIKM
1997
Springer
15 years 3 months ago
Learning Belief Networks from Data: An Information Theory Based Approach
This paper presents an efficient algorithm for learning Bayesian belief networks from databases. The algorithm takes a database as input and constructs the belief network structur...
Jie Cheng, David A. Bell, Weiru Liu