Paper available: Local Smoothing of RBF Networks

[Mark Orr]

The following paper has been accepted for presentation
at the International Symposium on Neural Networks,
Hsinchu, Taiwan, December 1995.


    LOCAL SMOOTHING OF RADIAL BASIS FUNCTION NETWORKS

                    Mark J.L. Orr

             Centre for Cognitive Science
                 Edinburgh University

  Abstract: A method of supervised learning is described
  which enhances generalisation performance by adaptive
  local smoothing in the input space. The method exploits
  the local nature of radial basis functions and employs
  multiple smoothing parameters optimised by generalised
  cross-validation. More traditional approaches have only
  a single smoothing parameter and produce a globally
  uniform smoothing but are demonstrably less effective
  unless the target function itself is uniformly smooth.


A postscript version of a slightly longer version (9 pages
instead of 6) can be retrieved by following the links
"publications" and "Neural Networks" from the world wide
web page:

  http://www.cns.ed.ac.uk/people/mark.html

Alternatively the paper can be retrieved by anonymous ftp:

  ftp://scott.cogsci.ed.ac.uk/pub/mjo/isann95-long.ps.Z

Size: 77KB compressed, 155KB uncompressed.

Sorry, no hardcopies.

----
Mark J L Orr, Centre for Cognitive Science, Edinburgh University,
2, Buccleuch Place, Edinburgh EH8 9LW, Scotland, UK
phone: (+44) (0) 131 650 4413 email: mjo at cns.ed.ac.uk