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» Feature selection for ranking using boosted trees
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AAAI
2000
15 years 1 months ago
Selective Sampling with Redundant Views
Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a ...
Ion Muslea, Steven Minton, Craig A. Knoblock
SIGIR
2004
ACM
15 years 5 months ago
Learning effective ranking functions for newsgroup search
Web communities are web virtual broadcasting spaces where people can freely discuss anything. While such communities function as discussion boards, they have even greater value as...
Wensi Xi, Jesper Lind, Eric Brill
TIP
2008
142views more  TIP 2008»
14 years 11 months ago
Image Feature Localization by Multiple Hypothesis Testing of Gabor Features
Several novel and particularly successful object and object category detection and recognition methods based on image features, local descriptions of object appearance, have recent...
Jarmo Ilonen, Joni-Kristian Kamarainen, Pekka Paal...
CIKM
2009
Springer
15 years 6 months ago
Web search result summarization: title selection algorithms and user satisfaction
Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user’s search process. We present a machine-learned algorithm that trains ...
Tapas Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawren...
WWW
2008
ACM
16 years 14 days ago
Recommending questions using the mdl-based tree cut model
The paper is concerned with the problem of question recommendation. Specifically, given a question as query, we are to retrieve and rank other questions according to their likelih...
Yunbo Cao, Huizhong Duan, Chin-Yew Lin, Yong Yu, H...