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UID:5522@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20131108T140000
DTEND;TZID=Europe/Paris:20131108T150000
DTSTAMP:20241030T092635Z
URL:https://www.i2m.univ-amu.fr/evenements/a-rabaoui-institut-fresnel-at-c
 mi-functional-data-analysis-via-bayesian-nonparametrics-with-application-t
 o-signal-classification/
SUMMARY: (...): A. Rabaoui (Institut Fresnel) at CMI: Functional Data Analy
 sis via Bayesian nonparametrics with application to signal classification
DESCRIPTION:: Functional Data Analysis via Bayesian nonparametrics with app
 lication to signal classification\nBy A. Rabaoui\\\, Institut Fresnel\\\, 
 Marseille.\n\nIn many signal processing applications\\\, data collected ar
 e drawn from continuous processes and often obtained in the form of functi
 ons. Functional data analysis (FDA) is a very attractive field of research
  that provides the possibility to fully exploit structure in such inherent
 ly continuous data. While more traditional approaches of functional data a
 nalysis are parametric and require specifying in advance a basis function 
 for the data\\\, challenges in modern signal processing applications motiv
 ate the nonparametric analysis of these data. In this talk\\\, I will show
  how to deal with functional classification problems from a Bayesian nonpa
 rametric approach. Then\\\, I will provide theoretical and practical motiv
 ations for our approach using Dirichlet process mixtures and Gaussian proc
 esses. Finally\\\, I will illustrate experimentally that the Bayesian nonp
 arametric FDA framework is particularly relevant for signal processing app
 lications where attributes (features) are really functions and can be depe
 ndent of each others.
CATEGORIES:Séminaire,Signal et Apprentissage
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