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SDM
2009
SIAM
112views Data Mining» more  SDM 2009»
14 years 4 months ago
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluat...
Frederik Janssen, Johannes Fürnkranz
AAAI
2011
12 years 7 months ago
CCRank: Parallel Learning to Rank with Cooperative Coevolution
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coev...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
ECIR
2011
Springer
12 years 11 months ago
Learning Models for Ranking Aggregates
Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate ...
Craig Macdonald, Iadh Ounis
ECIR
2010
Springer
13 years 7 months ago
Learning to Distribute Queries into Web Search Nodes
Web search engines are composed of a large set of search nodes and a broker machine that feeds them with queries. A location cache keeps minimal information in the broker to regist...
Marcelo Mendoza, Mauricio Marín, Flavio Fer...
ETAI
2000
84views more  ETAI 2000»
13 years 7 months ago
Learning Stochastic Logic Programs
Stochastic logic programs combine ideas from probabilistic grammars with the expressive power of definite clause logic; as such they can be considered as an extension of probabili...
Stephen Muggleton