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» Comparing relevance feedback algorithms for web search
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WWW
2011
ACM
13 years 2 months ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
GECCO
2009
Springer
109views Optimization» more  GECCO 2009»
14 years 15 days ago
A genetic algorithm for learning significant phrase patterns in radiology reports
Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. R...
Robert M. Patton, Thomas E. Potok, Barbara G. Beck...
ICML
2003
IEEE
14 years 8 months ago
Stochastic Local Search in k-Term DNF Learning
A novel native stochastic local search algorithm for solving k-term DNF problems is presented. It is evaluated on hard k-term DNF problems that lie on the phase transition and com...
Stefan Kramer, Ulrich Rückert
SEMWEB
2009
Springer
14 years 2 months ago
Using Naming Authority to Rank Data and Ontologies for Web Search
Abstract. The focus of web search is moving away from returning relevant documents towards returning structured data as results to user queries. A vital part in the architecture of...
Andreas Harth, Sheila Kinsella, Stefan Decker
WEBI
2009
Springer
14 years 2 months ago
Full-Subtopic Retrieval with Keyphrase-Based Search Results Clustering
We consider the problem of retrieving multiple documents relevant to the single subtopics of a given web query, termed “full-subtopic retrieval”. To solve this problem we pres...
Andrea Bernardini, Claudio Carpineto, Massimiliano...