Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Model of Information Retrieval
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{cummins:2004:lbp,
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author = "Ronan Cummins and Colm O'Riordan",
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title = "Using Genetic Programming to Evolve Weighting Schemes
for the Vector Space Model of Information Retrieval",
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booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
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year = "2004",
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editor = "Maarten Keijzer",
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address = "Seattle, Washington, USA",
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month = "26 " # jul,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP038.pdf",
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abstract = "Term weighting in many Information Retrieval models is
of crucial importance in the research and development
of accurate retrieval systems. This paper explores a
method to automatically determine suitable term
weighting schemes for the vector space model. Genetic
Programming is used to automatically evolve weighting
schemes that return a high average precision. These
weighting functions are tested on well-known test
collections and compared to the tf-idf based weighting
scheme using standard Information Retrieval performance
metrics.",
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notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
- }
Genetic Programming entries for
Ronan Cummins
Colm O'Riordan
Citations