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» Improving heuristic mini-max search by supervised learning
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SIGIR
2008
ACM
13 years 7 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
CIKM
2009
Springer
14 years 2 months ago
Semi-supervised learning of semantic classes for query understanding: from the web and for the web
Understanding intents from search queries can improve a user’s search experience and boost a site’s advertising profits. Query tagging via statistical sequential labeling mode...
Ye-Yi Wang, Raphael Hoffmann, Xiao Li, Jakub Szyma...
CIKM
2010
Springer
13 years 5 months ago
Discovery of numerous specific topics via term co-occurrence analysis
We describe efficient techniques for construction of large term co-occurrence graphs, and investigate an application to the discovery of numerous fine-grained (specific) topics. A...
Omid Madani, Jiye Yu
GECCO
2007
Springer
150views Optimization» more  GECCO 2007»
14 years 1 months ago
Credit assignment in adaptive memetic algorithms
Adaptive Memetic Algorithms couple an evolutionary algorithm with a number of local search heuristics for improving the evolving solutions. They are part of a broad family of meta...
J. E. Smith
WWW
2008
ACM
14 years 8 months ago
Unsupervised query segmentation using generative language models and wikipedia
In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. We use a generative query model to recover a query's underlyin...
Bin Tan, Fuchun Peng