Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Model of Information Retrieval

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@InProceedings{cummins:2004:lbp,
  author =       "Ronan Cummins and Colm O'Riordan",
  title =        "Using Genetic Programming to Evolve Weighting Schemes
                 for the Vector Space Model of Information Retrieval",
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP038.pdf",
  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.",
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",
}

Genetic Programming entries for Ronan Cummins Colm O'Riordan

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