A Connection between GRBF and MLP

  title={A Connection between GRBF and MLP},
  author={Minoru Maruyama and Federico Girosi and Tomaso A. Poggio}
Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper 'is to show that for normalized inputs, multilayer perceptron networks are radial function networks albeit wth a non-standard radial function). This provides an interpretation of the weights u7 as centers t of the radial function network, and therefore as equivalent to templates. This 'Insight may be… CONTINUE READING
Highly Cited
This paper has 18 citations. REVIEW CITATIONS
13 Citations
15 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 13 extracted citations


Publications referenced by this paper.
Showing 1-10 of 15 references

Projection-based approximation and a duality ith kernel meAhods

  • D. L. Donoho, I. M. Johnstone
  • The Annals of Statisticsi 17(l)-.58-106
  • 1989

Spline Smoothing and Nonparametric Regression

  • R. L. Eubank
  • volume 90 of Statistics, textbooks and onographs…
  • 1988

Nonlinear Approximation Theory- Springer-Verlag

  • D. Braess
  • Berlin
  • 1986
1 Excerpt

Similar Papers

Loading similar papers…