Skip to Main content Skip to Navigation
Journal articles

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [30 references]  Display  Hide  Download

https://hal.inria.fr/inria-00000599
Contributor : Fabrice Rossi <>
Submitted on : Sunday, September 23, 2007 - 3:02:49 PM
Last modification on : Wednesday, September 23, 2020 - 4:30:33 AM
Long-term archiving on: : Tuesday, September 21, 2010 - 1:49:29 PM

Files

fmlp-neural-networks-preprint....
Files produced by the author(s)

Identifiers

Collections

Citation

Fabrice Rossi, Brieuc Conan-Guez. Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis. Neural Networks, Elsevier, 2005, 18 (1), pp.45--60. ⟨10.1016/j.neunet.2004.07.001⟩. ⟨inria-00000599v2⟩

Share

Metrics

Record views

601

Files downloads

648