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» Feature selection for ranking using boosted trees
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ICPR
2008
IEEE
15 years 6 months ago
Ranking the local invariant features for the robust visual saliencies
Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and ...
Shengping Xia, Peng Ren, Edwin R. Hancock
ICPR
2004
IEEE
16 years 25 days ago
Face Pose Estimation and its Application in Video Shot Selection
In this paper, a face pose estimation method and its application in video shot selection for face image preprocessing is introduced. The pose estimator is learned by a boosting re...
Bo Wu, Haizhou Ai, Lianhong Cai, Shihong Lao, Zhig...
EMNLP
2004
15 years 1 months ago
A Boosting Algorithm for Classification of Semi-Structured Text
The focus of research in text classification has expanded from simple topic identification to more challenging tasks such as opinion/modality identification. Unfortunately, the la...
Taku Kudo, Yuji Matsumoto
NAACL
2007
15 years 1 months ago
A Probabilistic Framework for Answer Selection in Question Answering
This paper describes a probabilistic answer selection framework for question answering. In contrast with previous work using individual resources such as ontologies and the Web to...
Jeongwoo Ko, Luo Si, Eric Nyberg
ACL
2004
15 years 1 months ago
Convolution Kernels with Feature Selection for Natural Language Processing Tasks
Convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing (NLP) tasks. Experiments have, howev...
Jun Suzuki, Hideki Isozaki, Eisaku Maeda