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» Structured metric learning for high dimensional problems
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JAIR
2010
131views more  JAIR 2010»
13 years 6 months ago
Automatic Induction of Bellman-Error Features for Probabilistic Planning
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...
Jia-Hong Wu, Robert Givan
BMCBI
2006
134views more  BMCBI 2006»
13 years 7 months ago
Application of machine learning in SNP discovery
Background: Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given ...
Lakshmi K. Matukumalli, John J. Grefenstette, Davi...
COLT
2010
Springer
13 years 5 months ago
Forest Density Estimation
We study graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical models. For density estim...
Anupam Gupta, John D. Lafferty, Han Liu, Larry A. ...
AI
2009
Springer
14 years 2 months ago
Opinion Learning without Emotional Words
This paper shows that a detailed, although non-emotional, description of event or an action can be a reliable source for learning opinions. Empirical results show the practical uti...
Marina Sokolova, Guy Lapalme
ACCV
2010
Springer
13 years 2 months ago
Descriptor Learning Based on Fisher Separation Criterion for Texture Classification
Abstract. This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fish...
Yimo Guo, Guoying Zhao, Matti Pietikäinen, Zh...