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Estimating classification images with generalized linear and additive models.: Classification images with GLM and GAM

Abstract : 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.
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https://www.hal.inserm.fr/inserm-00323633
Contributor : Kenneth Knoblauch <>
Submitted on : Friday, March 26, 2010 - 11:44:09 AM
Last modification on : Monday, June 15, 2020 - 2:48:03 PM
Long-term archiving on: : Tuesday, September 28, 2010 - 11:52:05 AM

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Kenneth Knoblauch, Laurence 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⟩

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