Abstract
In many situations, high dimensional data can be considered as sampled functions. We recall in this paper how to implement a Multi-Layer Perceptron (MLP) on such data by approximating a theoretical MLP on functions thanks to basis expansion. We illustrate the proposed method on a phoneme discrimination problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ferraty, F., and Vieu, P. (2002). “Curves Discrimination: A Nonparametric Functional Approach,” to appear in Computational Statistics and Data Analysis.
Hastie, T., Buja, A., and Tibshirani, R. (1995). “Penalized Discriminant Analysis,” Annals of Statistics 23, 73–102.
Ramsay, J., and Silverman, B. (1997). Functional Data Analysis, Springer Series in Statistics, Springer, New York.
Rossi, F., and Conan-Guez, B. (2002). “Multi-Layer Perceptrons for Functional Data Analysis: A Projection-Based Approach,” in Artificial Neural Networks— ICANN 2002, ed. J. Dorronsoro, International Conference, Lecture Notes in Computer Science, Berlin: Springer, pp. 661–666.
Rossi, F., and Conan-Guez, B. (2003). “Functional Multi-Layer Perceptron: A Nonlinear Tool for Functional Data Analysis,” Technical Report 0331, September, LISE/CEREMADE, http://www.ceremade.dauphine.fr/.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Conan-Guez, B., Rossi, F. (2004). Phoneme Discrimination with Functional Multi-Layer Perceptrons. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-17103-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22014-5
Online ISBN: 978-3-642-17103-1
eBook Packages: Springer Book Archive