Radial Basis Function Neural Network Training Algorithm

A Radial Basis Function Neural Network Training Algorithm is a neural network training algorithm that can train a radial basis function neural network.

• AKA: Radial Basis Function NNet Trainer.
• Context:
  • It can be applied by a Radial Basis Function Neural Network Training System.
• Example(s):
  • …
• Counter-Example(s):
  • Feed-Forward Network Trainer.
  • Logistic ANN Training Algorithm.
  • Hyperbolic Tangent ANN Training Algorithm.
• See: Convolution NNet Training Algorithm, Regularized Least-Squares.

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References

1998

• (Chen, Chng & Alkadhimi, 1996) ⇒ S. Chen, E. S. Chng, and Khalil Alkadhimi. (1996), "Regularized Orthogonal Least Squares Algorithm for Constructing Radial Basis Function Retworks.” In: International Journal of Control, 64(5). doi:10.1080/00207179608921659
  • ABSTRACT: The paper presents a regularized orthogonal least squares learning algorithm for radial basis function networks. The proposed algorithm combines the advantages of both the orthogonal forward regression and regularization methods to provide an efficient and powerful procedure for constructing parsimonious network models that generalize well. Examples of nonlinear modelling and prediction are used to demonstrate better generalization performance of this regularized orthogonal least squares algorithm over the unregularized one.

1997

• (Mitchell, 1997) ⇒ Tom M. Mitchell. (1997). “Machine Learning." McGraw-Hill. . ISBN:0070428077
  • QUOTE: … an eager learning method the method for learning radial basis function networks. We call this method eager because it generalize beyond the training data before observe the new query, committing at training time to the network structure and weights that define its approximation to the target function.

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