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» On learning algorithm selection for classification
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ATAL
2006
Springer
15 years 10 months ago
Learning the required number of agents for complex tasks
Coordinating agents in a complex environment is a hard problem, but it can become even harder when certain characteristics of the tasks, like the required number of agents, are un...
Sébastien Paquet, Brahim Chaib-draa
FGR
2004
IEEE
167views Biometrics» more  FGR 2004»
15 years 10 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...
CVPR
2010
IEEE
16 years 3 months ago
Unsupervised Learning of Invariant Features Using Video
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...
David Stavens, Sebastian Thrun
DCC
2009
IEEE
16 years 7 months ago
Compressed Kernel Perceptrons
Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...
Slobodan Vucetic, Vladimir Coric, Zhuang Wang
ATAL
2008
Springer
15 years 8 months ago
A few good agents: multi-agent social learning
In this paper, we investigate multi-agent learning (MAL) in a multi-agent resource selection problem (MARS) in which a large group of agents are competing for common resources. Si...
Jean Oh, Stephen F. Smith