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» Search Engines that Learn from Implicit Feedback
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WWW
2001
ACM
15 years 11 months ago
Learning search engine specific query transformations for question answering
We introduce a method for learning query transformations that improves the ability to retrieve answers to questions from an information retrieval system. During the training stage...
Eugene Agichtein, Steve Lawrence, Luis Gravano
SIGIR
2002
ACM
14 years 10 months ago
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
In this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search. Two specific techniques are ex...
Ryen White, Ian Ruthven, Joemon M. Jose
104
Voted
ECCV
2002
Springer
16 years 29 days ago
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
Hedvig Sidenbladh, Michael J. Black, Leonid Sigal
AIEDAM
2011
14 years 6 months ago
Discovering implicit constraints in design
In familiar design domains, expert designers are able to quickly focus on “good designs”, based on constraints they have learned while exploring the design space. This ability ...
Madan Mohan Dabbeeru, Amitabha Mukerjee
EDBT
2006
ACM
169views Database» more  EDBT 2006»
15 years 11 months ago
Feedback-Driven Structural Query Expansion for Ranked Retrieval of XML Data
Relevance Feedback is an important way to enhance retrieval quality by integrating relevance information provided by a user. In XML retrieval, feedback engines usually generate an ...
Ralf Schenkel, Martin Theobald