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Related Experiment Videos

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

K Z Mao1, Guang-Bin Huang

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. ekzmao@ntu.edu.sg

IEEE Transactions on Neural Networks
|December 14, 2005
PubMed
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Selecting hidden layer neurons for radial basis function neural networks is crucial. This study proposes a data structure preserving method to improve network generalization by maintaining separation margins.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Radial basis function neural networks (RBFNNs) are widely used for classification and regression tasks.
  • A key challenge in RBFNN training is the optimal selection of hidden layer neurons.
  • Suboptimal neuron selection can lead to poor generalization performance.

Purpose of the Study:

  • To propose a novel method for selecting hidden layer neurons in RBFNNs.
  • To enhance the generalization capability of RBFNNs by preserving data structure.
  • To ensure that the selected neuron subset effectively represents class separation margins.

Main Methods:

  • The proposed method selects hidden layer neurons based on a data structure preserving criterion.
  • This criterion focuses on maintaining the relative locations of samples in high-dimensional space.

Related Experiment Videos

  • Emphasis is placed on preserving samples near class separation boundaries.

    • Main Results:

    • The selected subset of hidden layer neurons retains the separation margin of the full neuron set.
    • Networks trained with the proposed method demonstrate improved generalization.
    • Preserving data structure, especially near boundaries, is key to better performance.

    Conclusions:

    • The data structure preserving criterion offers an effective approach for hidden layer neuron selection in RBFNNs.
    • This method leads to RBFNNs with enhanced generalization capabilities.
    • The findings highlight the importance of maintaining data structure for robust neural network training.