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.
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Zufiria, P.J., Rivero, C. (2002). EM-Based Radial Basis Function Training with Partial Information. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_100
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DOI: https://doi.org/10.1007/3-540-46084-5_100
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