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» Modeling Classification and Inference Learning
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101
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JMLR
2010
172views more  JMLR 2010»
14 years 7 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
CVPR
2007
IEEE
16 years 3 months ago
Learning Visual Representations using Images with Captions
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
Ariadna Quattoni, Michael Collins, Trevor Darrell
114
Voted
ICML
2010
IEEE
15 years 2 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
ICML
2010
IEEE
15 years 2 months ago
A Conditional Random Field for Multiple-Instance Learning
We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines di...
Thomas Deselaers, Vittorio Ferrari
IJCINI
2007
125views more  IJCINI 2007»
15 years 26 days ago
A Unified Approach To Fractal Dimensions
The Cognitive Processes of Abstraction and Formal Inferences J. A. Anderson: A Brain-Like Computer for Cognitive Software Applications: the Resatz Brain Project L. Flax: Cognitive ...
Witold Kinsner