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78
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JMLR
2010
108views more  JMLR 2010»
14 years 5 months ago
Feature Selection using Multiple Streams
Feature selection for supervised learning can be greatly improved by making use of the fact that features often come in classes. For example, in gene expression data, the genes wh...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
93
Voted
ACL
2011
14 years 1 months ago
Learning Word Vectors for Sentiment Analysis
Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and...
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Da...
101
Voted
CVPR
2010
IEEE
15 years 6 months ago
What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes
We present two novel methods to automatically learn spatio-temporal dependencies of moving agents in complex dynamic scenes. They allow to discover temporal rules, such as the rig...
Daniel Kuettel, Michael Breitenstein, Luc Van Gool...
87
Voted
ISBI
2002
IEEE
15 years 11 months ago
Statistical shape model for automatic skull-stripping of brain images
This paper presents a statistical shape model for automatic skull stripping of MR brain images. A surface model of the brain boundary is hierarchically represented by a set of ove...
Zhiqiang Lao, Dinggang Shen, Christos Davatzikos
63
Voted
PAMI
2006
90views more  PAMI 2006»
14 years 10 months ago
Selection of Generative Models in Classification
This paper is concerned with the selection of a generative model for supervised classification. Classical criteria for model selection assess the fit of a model rather than its abi...
Guillaume Bouchard, Gilles Celeux