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» Evaluating algorithms that learn from data streams
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INFFUS
2008
97views more  INFFUS 2008»
15 years 6 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
NIPS
2003
15 years 8 months ago
Learning with Local and Global Consistency
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
ESCIENCE
2007
IEEE
16 years 27 days ago
Distributed and Generic Maximum Likelihood Evaluation
This paper presents GMLE 1 , a generic and distributed framework for maximum likelihood evaluation. GMLE is currently being applied to astroinformatics for determining the shape o...
Travis J. Desell, Nathan Cole, Malik Magdon-Ismail...
CVPR
2003
IEEE
16 years 8 months ago
Learning Bayesian Network Classifiers for Facial Expression Recognition using both Labeled and Unlabeled Data
Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through...
Ira Cohen, Nicu Sebe, Fabio Gagliardi Cozman, Marc...
ICMLA
2009
15 years 4 months ago
Mahalanobis Distance Based Non-negative Sparse Representation for Face Recognition
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some application...
Yangfeng Ji, Tong Lin, Hongbin Zha