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» Making inferences with small numbers of training sets
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CVPR
2001
IEEE
14 years 10 months ago
Learning Models for Object Recognition
We consider learning models for object recognition from examples. Our method is motivated by systems that use the Hausdorff distance as a shape comparison measure. Typically an ob...
Pedro F. Felzenszwalb
SAC
2009
ACM
14 years 3 months ago
Static type inference for Ruby
Many general-purpose, object-oriented scripting languages are dynamically typed, to keep the language flexible and not reject any programs unnecessarily. However, dynamic typing ...
Michael Furr, Jong-hoon (David) An, Jeffrey S. Fos...
AAAI
2007
13 years 11 months ago
Semi-Supervised Learning with Very Few Labeled Training Examples
In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled e...
Zhi-Hua Zhou, De-Chuan Zhan, Qiang Yang
FLAIRS
2006
13 years 10 months ago
Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning
A major difficulty in building Bayesian network models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with th...
Adam Zagorecki, Mark Voortman, Marek J. Druzdzel
ICCV
2003
IEEE
14 years 10 months ago
Inferring 3D Structure with a Statistical Image-Based Shape Model
We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure" model. The 3D shape of an object class is represented by sets...
Kristen Grauman, Gregory Shakhnarovich, Trevor Dar...