Lazy training of radial basis neural networks

Authors:
José M. Valls;Inés M. Galván;Pedro Isasi
Affiliations:
Departamento de Informática, Universidad Carlos III de Madrid, Leganés (Madrid), Spain;Departamento de Informática, Universidad Carlos III de Madrid, Leganés (Madrid), Spain;Departamento de Informática, Universidad Carlos III de Madrid, Leganés (Madrid), Spain
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 6
Cited 1

Instance-Based Learning Algorithms

Machine Learning
Approximation and radial-basis-function networks

Neural Computation
Local learning algorithms

Neural Computation
A sequential learning scheme for function approximation using minimal radial basis function neural networks

Neural Computation
Locally Weighted Learning

Artificial Intelligence Review - Special issue on lazy learning
Fast learning in networks of locally-tuned processing units

Neural Computation

Learning radial basis neural networks in a lazy way: A comparative study

Neurocomputing

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Abstract

Usually, training data are not evenly distributed in the input space. This makes non-local methods, like Neural Networks, not very accurate in those cases. On the other hand, local methods have the problem of how to know which are the best examples for each test pattern. In this work, we present a way of performing a trade off between local and non-local methods. On one hand a Radial Basis Neural Network is used like learning algorithm, on the other hand a selection of the training patterns is used for each query. Moreover, the RBNN initialization algorithm has been modified in a deterministic way to eliminate any initial condition influence. Finally, the new method has been validated in two time series domains, an artificial and a real world one.