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» Feature selection for ranking using boosted trees
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CVPR
2007
IEEE
16 years 1 months ago
Eigenboosting: Combining Discriminative and Generative Information
A major shortcoming of discriminative recognition and detection methods is their noise sensitivity, both during training and recognition. This may lead to very sensitive and britt...
Helmut Grabner, Peter M. Roth, Horst Bischof
CVPR
2008
IEEE
16 years 1 months ago
Discovering class specific composite features through discriminative sampling with Swendsen-Wang Cut
This paper proposes a novel approach to discover a set of class specific "composite features" as the feature pool for the detection and classification of complex objects...
Feng Han, Ying Shan, Harpreet S. Sawhney, Rakesh K...
126
Voted
CVPR
2009
IEEE
16 years 6 months ago
Boosted Multi-Task Learning for Face Verification With Applications to Web Image and Video Search
Face verification has many potential applications including filtering and ranking image/video search results on celebrities. Since these images/videos are taken under uncontrolle...
Xiaogang Wang (MIT), Cha Zhang (Microsoft Research...
ECIR
2011
Springer
14 years 3 months ago
Fractional Similarity: Cross-Lingual Feature Selection for Search
Abstract. Training data as well as supplementary data such as usagebased click behavior may abound in one search market (i.e., a particular region, domain, or language) and be much...
Jagadeesh Jagarlamudi, Paul N. Bennett
79
Voted
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
15 years 6 months ago
The Feature Importance Ranking Measure
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
Alexander Zien, Nicole Krämer, Sören Son...