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» Maximum Margin Ranking Algorithms for Information Retrieval
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WWW
2008
ACM
14 years 8 months ago
Learning to rank relational objects and its application to web search
Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becom...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
SIGIR
2009
ACM
14 years 2 months ago
Risky business: modeling and exploiting uncertainty in information retrieval
Most retrieval models estimate the relevance of each document to a query and rank the documents accordingly. However, such an approach ignores the uncertainty associated with the ...
Jianhan Zhu, Jun Wang, Ingemar J. Cox, Michael J. ...
PAMI
2012
11 years 10 months ago
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
João Carreira, Cristian Sminchisescu
DS
2001
143views Database» more  DS 2001»
13 years 9 months ago
WebSifter: An Ontological Web-Mining Agent for E-Business
: The World Wide Web provides access to a great deal of information on a vast array of subjects. A user can begin a search for information by selecting a Web page and following the...
Anthony Scime, Larry Kerschberg
WWW
2006
ACM
14 years 8 months ago
Topical TrustRank: using topicality to combat web spam
Web spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the s...
Baoning Wu, Vinay Goel, Brian D. Davison