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» Learning and Generalization with the Information Bottleneck
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CIKM
2008
Springer
15 years 6 hour ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
MIR
2010
ACM
207views Multimedia» more  MIR 2010»
14 years 8 months ago
Learning to rank for content-based image retrieval
In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking st...
Fabio F. Faria, Adriano Veloso, Humberto Mossri de...
SIGIR
2008
ACM
14 years 10 months ago
Learning query intent from regularized click graphs
This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a ...
Xiao Li, Ye-Yi Wang, Alex Acero
CIKM
2010
Springer
14 years 8 months ago
Who should I cite: learning literature search models from citation behavior
Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide ...
Steven Bethard, Dan Jurafsky
WWW
2002
ACM
15 years 10 months ago
A machine learning based approach for table detection on the web
Table is a commonly used presentation scheme, especially for describing relational information. However, table understanding remains an open problem. In this paper, we consider th...
Yalin Wang, Jianying Hu