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CVPR
2001
IEEE
15 years 11 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
15 years 2 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
14 years 10 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
14 years 11 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»
14 years 9 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