Estimating classification images with generalized linear and additive models. - Inserm - Institut national de la santé et de la recherche médicale Accéder directement au contenu
Article Dans Une Revue Journal of vision (Charlottesville, Va.) Année : 2008

Estimating classification images with generalized linear and additive models.

Résumé

Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal noise, however, the advantage of the GLM over the LM decreases and GLM is no more accurate than LM. We then introduce the Generalized Additive Model (GAM), an extension of GLM that can be used to estimate smooth classification images adaptively. We show that this approach is more robust to the presence of internal noise, and finally, we demonstrate that GAM is readily adapted to estimation of higher order (nonlinear) classification images and to testing their significance.
Fichier principal
Vignette du fichier
KnoblauchMaloney.INPRESS.JOV.2008.pdf (323.84 Ko) Télécharger le fichier
inserm-00323633_edited.pdf (1.22 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

inserm-00323633 , version 1 (26-03-2010)

Identifiants

Citer

Kenneth Knoblauch, Laurence T. Maloney. Estimating classification images with generalized linear and additive models.: Classification images with GLM and GAM. Journal of vision (Charlottesville, Va.), 2008, 8 (16), pp.10.1-19. ⟨10.1167/8.16.10⟩. ⟨inserm-00323633⟩
140 Consultations
305 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More