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» Feature selection for ranking using boosted trees
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EMNLP
2009
14 years 9 months ago
Reverse Engineering of Tree Kernel Feature Spaces
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
Daniele Pighin, Alessandro Moschitti
SEMWEB
2010
Springer
14 years 9 months ago
Using Reformulation Trees to Optimize Queries over Distributed Heterogeneous Sources
Abstract. In order to effectively and quickly answer queries in environments with distributed RDF/OWL, we present a query optimization algorithm to identify the potentially relevan...
Yingjie Li, Jeff Heflin
BMCBI
2010
126views more  BMCBI 2010»
14 years 12 months ago
A boosting method for maximizing the partial area under the ROC curve
Background: The receiver operating characteristic (ROC) curve is a fundamental tool to assess the discriminant performance for not only a single marker but also a score function c...
Osamu Komori, Shinto Eguchi
CVPR
2001
IEEE
16 years 1 months ago
Learning Representative Local Features for Face Detection
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...
ICCV
2005
IEEE
15 years 5 months ago
Contour-Based Learning for Object Detection
We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudiment...
Jamie Shotton, Andrew Blake, Roberto Cipolla