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» Ensemble Methods in Machine Learning
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92
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COLT
2005
Springer
15 years 6 months ago
A PAC-Style Model for Learning from Labeled and Unlabeled Data
Abstract. There has been growing interest in practice in using unlabeled data together with labeled data in machine learning, and a number of different approaches have been develo...
Maria-Florina Balcan, Avrim Blum
ICML
2010
IEEE
15 years 1 months ago
Nonparametric Return Distribution Approximation for Reinforcement Learning
Standard Reinforcement Learning (RL) aims to optimize decision-making rules in terms of the expected return. However, especially for risk-management purposes, other criteria such ...
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashim...
JMLR
2006
186views more  JMLR 2006»
15 years 17 days ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
104
Voted
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 10 days ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh
210
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GCB
2010
Springer
204views Biometrics» more  GCB 2010»
14 years 10 months ago
Learning Pathway-based Decision Rules to Classify Microarray Cancer Samples
: Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninfor...
Enrico Glaab, Jonathan M. Garibaldi, Natalio Krasn...