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» Training of Classifiers Using Virtual Samples Only
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CVPR
2009
IEEE
15 years 23 days ago
Learning Based Automatic Face Annotation for Arbitrary Poses and Expressions from Frontal Images Only
Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require ...
Akshay Asthana (Australian National University), R...
DIS
2008
Springer
13 years 7 months ago
Unsupervised Classifier Selection Based on Two-Sample Test
We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
Timo Aho, Tapio Elomaa, Jussi Kujala
CVPR
2008
IEEE
14 years 7 months ago
Semi-supervised boosting using visual similarity learning
The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semisupervise...
Christian Leistner, Helmut Grabner, Horst Bischof
BMCBI
2010
143views more  BMCBI 2010»
13 years 5 months ago
Learning gene regulatory networks from only positive and unlabeled data
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
Luigi Cerulo, Charles Elkan, Michele Ceccarelli
CORR
2011
Springer
183views Education» more  CORR 2011»
12 years 9 months ago
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the tra...
Foster J. Provost, Gary M. Weiss