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» Evaluating learning algorithms and classifiers
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ICML
2009
IEEE
14 years 5 months ago
Supervised learning from multiple experts: whom to trust when everyone lies a bit
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed...
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K...
GECCO
2003
Springer
126views Optimization» more  GECCO 2003»
14 years 4 months ago
Coevolution and Linear Genetic Programming for Visual Learning
In this paper, a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition s...
Krzysztof Krawiec, Bir Bhanu
AAAI
2007
14 years 1 months ago
Autonomous Development of a Grounded Object Ontology by a Learning Robot
We describe how a physical robot can learn about objects from its own autonomous experience in the continuous world. The robot identifies statistical regularities that allow it t...
Joseph Modayil, Benjamin Kuipers
ICML
2007
IEEE
14 years 11 months ago
Combining online and offline knowledge in UCT
The UCT algorithm learns a value function online using sample-based search. The TD() algorithm can learn a value function offline for the on-policy distribution. We consider three...
Sylvain Gelly, David Silver
FGR
2004
IEEE
167views Biometrics» more  FGR 2004»
14 years 2 months ago
Adaptive Learning of an Accurate Skin-Color Model
Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture t...
Qiang Zhu, Kwang-Ting Cheng, Ching-Tung Wu, Yi-Leh...