Neuron selection for RBF neural network classifier based on data structure preserving criterion.

K Z Mao; Guang-Bin Huang

doi:10.1109/tnn.2005.853575

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Neuron selection for RBF neural network classifier based on data structure preserving criterion.

K Z Mao·Guang-Bin Huang

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Abstract

The central problem in training a radial basis function neural network is the selection of hidden layer neurons. In this paper, we propose to select hidden layer neurons based on data structure preserving criterion. Data structure denotes relative location of samples in the high-dimensional space. By preserving the data structure of samples including those that are close to separation boundaries between different classes, the neuron subset selected retains the separation margin underlying the full set of hidden layer neurons. As a direct result, the network obtained tends to generalize well.

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Algorithms Cluster Analysis Computer Simulation Databases, Factual Information Storage and Retrieval Models, Theoretical Neural Networks, Computer Pattern Recognition, Automated Journal Article

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K Z Mao, Guang-Bin Huang. 2005. Neuron selection for RBF neural network classifier based on data structure preserving criterion.. https://doi.org/10.1109/tnn.2005.853575

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