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» Learning and Inference with Constraints
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CVPR
2010
IEEE
13 years 5 months ago
P-N learning: Bootstrapping binary classifiers by structural constraints
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the ...
Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
ICDM
2008
IEEE
150views Data Mining» more  ICDM 2008»
14 years 2 months ago
Pseudolikelihood EM for Within-network Relational Learning
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
Rongjing Xiang, Jennifer Neville
ADBIS
2005
Springer
112views Database» more  ADBIS 2005»
14 years 1 months ago
Non-destructive Integration of Form-Based Views
Form documents or screen forms bring essential information on the data manipulated by an organization. They can be considered as different but often overlapping views of its whole...
Jan Hidders, Jan Paredaens, Philippe Thiran, Geert...
COGSCI
2010
88views more  COGSCI 2010»
13 years 7 months ago
Domain-Creating Constraints
The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A lear...
Robert L. Goldstone, David Landy
ICML
2005
IEEE
14 years 8 months ago
Tempering for Bayesian C&RT
This paper concerns the experimental assessment of tempering as a technique for improving Bayesian inference for C&RT models. Full Bayesian inference requires the computation ...
Nicos Angelopoulos, James Cussens