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Conference papers

Boosting vs maximum de vraisemblance

Abstract : This article sets out to demonstrate how boosting can serve as a supervised classification method, and to compare its results with those of conventional methods. The comparison begins with a theoretical example drawn from remote sensing classification in which several criteria are varied: number of pixels per class, overlapping (or not) of radiometric values between classes, with and without spatial structuring of classes within the geographical space. It is seen that 1) maximum likelihood give better results than boosting when the radiometric values for each class are clearly separated. This advantage is lost as the number of pixels per class increases; 2) boosting is systematically better than maximum likelihood in the event of overlapping radiometric variable classes, whether or not there is a spatial structure.
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Contributor : Migration Irstea Publications <>
Submitted on : Friday, May 15, 2020 - 1:14:14 PM
Last modification on : Friday, April 16, 2021 - 3:29:23 AM


  • HAL Id : hal-02589111, version 1
  • IRSTEA : PUB00021695



M. Arnaud, Jean-Stéphane Bailly, C. Puech. Boosting vs maximum de vraisemblance. XXXVèmes Journées de statistique du 2 au 6 juin 2003, Lyon, 2003, pp.12. ⟨hal-02589111⟩



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