EM-Based Radial Basis Function Training with Partial Information

Authors:
Pedro J. Zufiria;Carlos Rivero
Affiliations:
-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 5
Cited 1

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Statistical analysis with missing data
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Accelerating the Convergence of EM-Based Training Algorithms for RBF Networks

IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I

A neural network-based framework for the reconstruction of incomplete data sets

Neurocomputing

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Abstract

This work presents an EM approach for nonlinear regression with incomplete data. Radial Basis Function (RBF) Neural Networks are employed since their architecture is appropriate for an efficient parameter estimation. The training algorithm expectation (E) step takes into account the censorship over the data, and the maximization (M) step can be implemented in several ways. The results guarantee the convergence of the algorithm in the GEM (Generalized EM) framework.