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» Estimating the Accuracy of Learned Concepts
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CVPR
2000
IEEE
15 years 11 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
CVPR
2007
IEEE
15 years 11 months ago
Discriminative Learning of Dynamical Systems for Motion Tracking
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
Minyoung Kim, Vladimir Pavlovic
ICML
2004
IEEE
15 years 10 months ago
Learning random walk models for inducing word dependency distributions
Many NLP tasks rely on accurately estimating word dependency probabilities P(w1|w2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the...
Kristina Toutanova, Christopher D. Manning, Andrew...
RAS
2010
216views more  RAS 2010»
14 years 8 months ago
A nonparametric learning approach to range sensing from omnidirectional vision
We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available dur...
Christian Plagemann, Cyrill Stachniss, Jürgen...
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ICIP
2002
IEEE
15 years 11 months ago
Learning a decision boundary for face detection
This paper describes a pattern classification approach for detecting frontal-view faces via learning a decision boundary. The classification can be achieved either by explicit est...
Tae-Kyun Kim, Donggeon Kong, Sang Ryong Kim