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SIGIR
2010
ACM
15 years 3 months ago
Positional relevance model for pseudo-relevance feedback
Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for ...
Yuanhua Lv, ChengXiang Zhai
COLT
2005
Springer
15 years 5 months ago
Analysis of Perceptron-Based Active Learning
We start by showing that in an active learning setting, the Perceptron algorithm needs Ω( 1 ε2 ) labels to learn linear separators within generalization error ε. We then prese...
Sanjoy Dasgupta, Adam Tauman Kalai, Claire Montele...
PVLDB
2010
104views more  PVLDB 2010»
14 years 10 months ago
MCDB-R: Risk Analysis in the Database
Enterprises often need to assess and manage the risk arising from uncertainty in their data. Such uncertainty is typically modeled as a probability distribution over the uncertain...
Peter J. Haas, Christopher M. Jermaine, Subi Arumu...
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VLDB
2002
ACM
138views Database» more  VLDB 2002»
14 years 11 months ago
Adaptable Similarity Search using Non-Relevant Information
Many modern database applications require content-based similarity search capability in numeric attribute space. Further, users' notion of similarity varies between search se...
T. V. Ashwin, Rahul Gupta, Sugata Ghosal
SIGIR
2012
ACM
13 years 2 months ago
Content-based retrieval for heterogeneous domains: domain adaptation by relative aggregation points
We introduce the problem of domain adaptation for content-based retrieval and propose a domain adaptation method based on relative aggregation points (RAPs). Content-based retriev...
Makoto P. Kato, Hiroaki Ohshima, Katsumi Tanaka