Functional multi-layer perceptron: a non-linear tool for functional data analysis

نویسندگان

  • Fabrice Rossi
  • Brieuc Conan-Guez
چکیده

In this paper, we study a natural extension of multi-layer perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results, which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.

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عنوان ژورنال:
  • Neural networks : the official journal of the International Neural Network Society

دوره 18 1  شماره 

صفحات  -

تاریخ انتشار 2005