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Communication Dans Un Congrès Année : 1996

Fuzzy techniques for coffee flavour classification

Résumé

This paper deals with the differentiation of pure and mixed samples of commercialized coffees. Fuzzy c-means and k-nearest neighbours algorithms are used to discriminate pure arabica and robusta samples by treating the initial feature vectors. When samples sets including mixtures are analysed, these techniques fail to differentiate efficiently this category of samples. A data processing by means of a discriminant analysis provides two new features which are used for classification purposes using the k-nearest neighbours algorithm
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Dates et versions

hal-00722535 , version 1 (02-08-2012)

Identifiants

  • HAL Id : hal-00722535 , version 1
  • PRODINRA : 244780

Citer

Lotfi Khodja, Laurent Foulloy, Eric Benoit, Thierry Talou. Fuzzy techniques for coffee flavour classification. 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems, Jul 1996, Granada, Spain. pp.709-714. ⟨hal-00722535⟩
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