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» Approximation Methods for Supervised Learning
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IJCNN
2006
IEEE
15 years 12 months ago
A Variational EM Approach to Predicting Uncertainty in Supervised Learning
— In many applications of supervised learning, the conditional average of the target variables is not sufficient for prediction. The dependencies between the explanatory variabl...
Markus Harva
DOCENG
2004
ACM
15 years 11 months ago
Supervised learning for the legacy document conversion
We consider the problem of document conversion from the renderingoriented HTML markup into a semantic-oriented XML annotation defined by user-specific DTDs or XML Schema descrip...
Boris Chidlovskii, Jérôme Fuselier
NIPS
2007
15 years 7 months ago
Statistical Analysis of Semi-Supervised Regression
Semi-supervised methods use unlabeled data in addition to labeled data to construct predictors. While existing semi-supervised methods have shown some promising empirical performa...
John D. Lafferty, Larry A. Wasserman
ICMLC
2005
Springer
15 years 11 months ago
Kernel-Based Metric Adaptation with Pairwise Constraints
Abstract. Many supervised and unsupervised learning algorithms depend on the choice of an appropriate distance metric. While metric learning for supervised learning tasks has a lon...
Hong Chang, Dit-Yan Yeung
JMLR
2010
153views more  JMLR 2010»
15 years 20 days ago
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly-labeled data. GE fits m...
Gideon S. Mann, Andrew McCallum