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PubMed · 12662694

A global learning algorithm for a RBF network.

Abstract

This article presents a new learning algorithm for the construction and training of a RBF neural network. The algorithm is based on a global mechanism of parameter learning using a maximum likelihood classification approach. The resulting neurons in the RBF network partitions a multidimensional pattern space into a set of maximum-size hyper-ellipsoid subspaces in terms of the statistical distributions of the training samples. An important feature of the algorithm is that the learning process includes both the tasks of discovering a suitable network structure and of determining the connection weights. The entire network and its parameters are thought of evolved gradually in the learning process.

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BibTeXRIS

Qiuming Zhu, Yao Cai, Luzheng Liu. 1999. A global learning algorithm for a RBF network.. https://doi.org/10.1016/s0893-6080(98)00146-4

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