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» Variations on U-Shaped Learning
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116
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IROS
2008
IEEE
144views Robotics» more  IROS 2008»
15 years 7 months ago
Learning nonparametric policies by imitation
— A long cherished goal in artificial intelligence has been the ability to endow a robot with the capacity to learn and generalize skills from watching a human teacher. Such an ...
David B. Grimes, Rajesh P. N. Rao
CVPR
2010
IEEE
15 years 6 months ago
Learning 3D Action Models from a few 2D videos for View Invariant Action Recognition
Most existing approaches for learning action models work by extracting suitable low-level features and then training appropriate classifiers. Such approaches require large amount...
Pradeep Natarajan, Vivek Singh, Ram Nevatia
115
Voted
GECCO
2008
Springer
184views Optimization» more  GECCO 2008»
15 years 1 months ago
Analysis of mammography reports using maximum variation sampling
A genetic algorithm (GA) was developed to implement a maximum variation sampling technique to derive a subset of data from a large dataset of unstructured mammography reports. It ...
Robert M. Patton, Barbara G. Beckerman, Thomas E. ...
114
Voted
ICCV
2009
IEEE
16 years 5 months ago
Level Set Segmentation with Both Shape and Intensity Priors
We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set. By applying Bayes’ rule...
Siqi Chen and Richard J. Radke
80
Voted
ICML
2007
IEEE
16 years 1 months ago
Linear and nonlinear generative probabilistic class models for shape contours
We introduce a robust probabilistic approach to modeling shape contours based on a lowdimensional, nonlinear latent variable model. In contrast to existing techniques that use obj...
Graham McNeill, Sethu Vijayakumar