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203
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CORR
2012
Springer
14 years 2 months ago
2012
Springer
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisatio...
200
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ATAL
2010
Springer
15 years 8 months ago
2010
Springer
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
209
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MICCAI
2009
Springer
16 years 2 months ago
2009
Springer
This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain imag...
215
Voted
CVPR
2003
IEEE
16 years 9 months ago
2003
IEEE
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
202
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ECCV
2000
Springer
16 years 9 months ago
2000
Springer
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of mode...