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2015 | 3 | 1 |

Tytuł artykułu

A classification method for binary predictors combining similarity measures and mixture models

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Abstrakty

EN
In this paper, a new supervised classification method dedicated to binary predictors is proposed. Its originality is to combine a model-based classification rule with similarity measures thanks to the introduction of new family of exponential kernels. Some links are established between existing similarity measures when applied to binary predictors. A new family of measures is also introduced to unify some of the existing literature. The performance of the new classification method is illustrated on two real datasets (verbal autopsy data and handwritten digit data) using 76 similarity measures.

Wydawca

Czasopismo

Rocznik

Tom

3

Numer

1

Opis fizyczny

Daty

otrzymano
2015-06-11
zaakceptowano
2015-11-20
online
2015-12-12

Twórcy

  • Inria Grenoble Rhône-Alpes & LJK, France
  • LERSTAD-UGB, Saint-Louis, Sénégal
  • URMITE-IRD, Dakar, Sénégal
  • Inria Grenoble Rhône-Alpes & LJK, France
  • LERSTAD-UGB, Saint-Louis, Sénégal
  • URMITE-IRD, Dakar, Sénégal
  • URMITE-IRD, Dakar, Sénégal

Bibliografia

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Typ dokumentu

Bibliografia

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Identyfikator YADDA

bwmeta1.element.doi-10_1515_demo-2015-0017
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