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Related Experiment Videos

Sensitivity analysis applied to the construction of radial basis function networks.

D Shi1, D S Yeung, J Gao

  • 1School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. asdmshi@ntu.edu.sg

Neural Networks : the Official Journal of the International Neural Network Society
|June 9, 2005
PubMed
Summary

This study introduces a new method for building radial basis function (RBF) networks using sensitivity analysis. This approach improves classification accuracy, matching support vector machine (SVM) performance.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Science

Background:

  • Traditional radial basis function (RBF) network construction relies on maximum likelihood learning for cluster centers.
  • This method can be suboptimal in determining the optimal number and placement of hidden neurons.

Purpose of the Study:

  • To propose a novel learning algorithm for RBF network construction utilizing sensitivity analysis.
  • To enhance the generalizability and accuracy of RBF classifiers for unknown data.

Main Methods:

  • A new algorithm determines hidden neuron count and centers by maximizing output sensitivity to training data.
  • Sensitivity analysis is employed during the training phase for RBF network construction.
  • For classification, minimal hidden neurons with maximal sensitivity are selected for optimal generalization.

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Main Results:

  • The proposed sensitivity-based RBF classifier demonstrates superior performance compared to conventional RBFs.
  • The accuracy of the sensitivity-based RBF classifier is comparable to that of support vector machines (SVMs).

Conclusions:

  • Sensitivity analysis offers a viable and effective alternative for constructing RBF networks.
  • This method enhances RBF network performance in classification tasks.