Abstract
Starting from the results obtained by recursive partitioning methods, radial basis function networks for longitudinal data are derived. The aim of this work is to show how the joint use of the two methods allows to overcome some drawbacks that they show when they are used separately. More precisely, this strategy allows not only to obtain a smooth nonparametric estimate of the regression surface, but also to automatically determine the model complexity and to perform a covariate selection. The performances of the proposed strategy are evaluated on simulated data sets.
Keywords
- Radial Basis Function
- Regression Tree
- Radial Basis Function Network
- Multivariate Adaptive Regression Spline
- Covariate Space
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
BISHOP, C. M. (1995): Neural Networks for Pattern Recognition. Clarendon Press, Oxford.
BREIMAN, L., FRIEDMAN, J. H., OLSHEN, R. A. and STONE, C. J. (1984): Classification and Regression Trees. Wadsworth, Belmont.
GALIMBERTI, G. and MONTANARI, A. (2002): Regression Trees for Longitudinal Data with Time-Dependent Covariates. In: K. Jajuga, A. Sokolowsky and H.-H. Bock (Eds.): Classification, Clustering and Data Analysis. Springer, Heidelberg, 391–398.
KUBAT, M. (1998): Decision Trees Can Initialize Radial-Basis-Function Networks. IEEE Transactions on Neural Networks, 9, 813–821.
MOODY, J. and DARKEN, C. J. (1989): Fast learning in Network of Locally-Tuned Processing Units. Neural Computation, 1, 281–297.
PILLATI, M. and CALÒ, D. G. (2001): A Robust Clustering Procedure for Centre Location in RBF Networks. In: C. Provasi (Ed.): Modelli complessi e metodi computazionali intensivi per la stima e la previsione. Cluep Editrice, Padova, 373–378.
PILLATI, M. and MIGLIO, R. (2000): Radial Basis Function Networks and Decision Trees in the Determination of a Classifier. In: H. A. L. Kiers, J.-P. Rasson, P. J. F. Groenen and M. Schader (Eds.): Data Analysis, Classification and Related Methods. Springer, Heidelberg, 211–216.
POGGIO, T. and GIROSI F. (1990): Networks for Approximation and Learning. Proceedings of the IEEE, 78, 1481–1497.
RIPLEY, B. D. (1996): Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge.
SEGAL, M. R. (1992): Tree-Structured Methods for Longitudinal Data. Journal of the American Statistical Association, 87, 407–418.
ZHANG, H. (1997): Multivariate Adaptive Splines for the Analysis of Longitudinal Data. Journal of Computational and Graphical Statistics, 6, 79–91.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pillati, M., Calò, D.G., Galimberti, G. (2003). Combining Regression Trees and Radial Basis Function Networks in Longitudinal Data Modelling. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-18991-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40354-8
Online ISBN: 978-3-642-18991-3
eBook Packages: Springer Book Archive