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Correcting the Training Data

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Pattern Recognition and String Matching

Part of the book series: Combinatorial Optimization ((COOP,volume 13))

Abstract

Traditionally, learning algorithms and pattern recognition methods have been sorted into two broad groups: supervised and unsupervised (predictive and informative in Data Mining terminology) whether training data is available or not. Supervised classifier design is based on the information supplied by a training sample (TS): a set of training patterns, instances or prototypes that are assumed to represent all the relevant classes and to bear correct class labels.

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Author information

Authors and Affiliations

  1. Instituto Tecnológico de Toluca, 52140, Metepec, México

    Ricardo Barandela, Eduardo Gasca & Roberto Alejo

  2. Instituto de Geografía Tropical, La Habana, Cuba

    Ricardo Barandela

Authors
  1. Ricardo Barandela
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  2. Eduardo Gasca
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  3. Roberto Alejo
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Editor information

Editors and Affiliations

  1. University of Wisconsin — Green Bay, Green Bay, WI, USA

    Dechang Chen

  2. The George Washington University, Washington DC, USA

    Xiuzhen Cheng

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© 2003 Kluwer Academic Publishers

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Barandela, R., Gasca, E., Alejo, R. (2003). Correcting the Training Data. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_1

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  • DOI: https://doi.org/10.1007/978-1-4613-0231-5_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7952-2

  • Online ISBN: 978-1-4613-0231-5

  • eBook Packages: Springer Book Archive

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