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IJAR
2010
130views more  IJAR 2010»
15 years 3 months ago
Learning locally minimax optimal Bayesian networks
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Tomi Silander, Teemu Roos, Petri Myllymäki
ALT
2004
Springer
16 years 1 months ago
Learning r-of-k Functions by Boosting
We investigate further improvement of boosting in the case that the target concept belongs to the class of r-of-k threshold Boolean functions, which answer “+1” if at least r o...
Kohei Hatano, Osamu Watanabe
CVPR
2006
IEEE
15 years 10 months ago
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
We present an algorithm for jointly learning a consistent bidirectional generative-recognition model that combines top-down and bottom-up processing for monocular 3d human motion ...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...
FUZZIEEE
2007
IEEE
15 years 11 months ago
Survey of Rough and Fuzzy Hybridization
— This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feat...
Pawan Lingras, Richard Jensen
OTM
2005
Springer
15 years 10 months ago
Taking Advantage of LOM Semantics for Supporting Lesson Authoring
Abstract. Learning Object Metadata (LOM) is an interoperable standard focused on enabling the reuse of learning material for authoring lessons. Nevertheless, few work was done on t...
Olivier Motelet, Nelson A. Baloian