Computer Science – Neural and Evolutionary Computing
Scientific paper
2007-09-23
Neural Networks 18, 1 (2005) 45--60
Computer Science
Neural and Evolutionary Computing
http://www.sciencedirect.com/science/journal/08936080
Scientific paper
10.1016/j.neunet.2004.07.001
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.
Conan-Guez Brieuc
Rossi Fabrice
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