Improving haar cascade classifiers through the synthesis of new training examples

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Correia:2012:GECCOcomp,
  author =       "Joao Correia and Penousal Machado and Juan Romero",
  title =        "Improving haar cascade classifiers through the
                 synthesis of new training examples",
  booktitle =    "GECCO Companion '12: Proceedings of the fourteenth
                 international conference on Genetic and evolutionary
                 computation conference companion",
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, Genetic programming: Poster",
  pages =        "1479--1480",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2331001",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A Genetic Programming approach for the improvement of
                 the performance of classifier systems through the
                 synthesis of new training instances is explored.
                 Genetic Programming is used to exploit shortcomings of
                 classifiers systems and generate misclassified
                 instances. The proposed approach performs multiple
                 parallel evolutionary runs to generate a large number
                 of potentially misclassified samples. A supervisor
                 module determines which of the generated images have
                 been misclassified and which should be added to the
                 training set. New classifiers are trained based on the
                 original training set augmented by the selected evolved
                 instances. The results attained while using face
                 detection classifiers are presented and discussed.
                 Overall they indicate that significant improvements are
                 attained when using multiple evolutionary runs.",
  notes =        "Also known as \cite{2331001} Distributed at
                 GECCO-2012.

                 ACM Order Number 910122.",
}

Genetic Programming entries for Joao Correia Lopes Penousal Machado Juan Jesus Romero Cardalda

Citations