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CVPR
2001
IEEE
14 years 8 months ago
Learning Models for Object Recognition
We consider learning models for object recognition from examples. Our method is motivated by systems that use the Hausdorff distance as a shape comparison measure. Typically an ob...
Pedro F. Felzenszwalb
ECCV
2010
Springer
13 years 12 months ago
Automatic Attribute Discovery and Characterization
It is common to use domain specific terminology – attributes – to describe the visual appearance of objects. In order to scale the use of these describable visual attributes t...
ICML
2010
IEEE
13 years 7 months ago
Boosting Classifiers with Tightened L0-Relaxation Penalties
We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...
Noam Goldberg, Jonathan Eckstein
COLT
2008
Springer
13 years 8 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál
JMLR
2006
117views more  JMLR 2006»
13 years 6 months ago
On the Complexity of Learning Lexicographic Strategies
Fast and frugal heuristics are well studied models of bounded rationality. Psychological research has proposed the take-the-best heuristic as a successful strategy in decision mak...
Michael Schmitt, Laura Martignon