TR announcement: Bayesian Self-Organisation

[luttrell@signal.dra.hmg.gb]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/luttrell.bayes-selforg.ps.Z

The file luttrell.bayes-selforg.ps.Z is now available for
copying from the Neuroprose repository:

A Bayesian Analysis of Self-Organising Maps (24 pages)
Stephen P Luttrell
Defence Research Agency, United Kingdom

ABSTRACT:
In this paper Bayesian methods are used to analyse some of the
properties of a special type of Markov chain. The forward transitions
through the chain are followed by inverse transitions (using Bayes'
theorem) backwards through a copy of the same chain; this is called
a folded Markov chain. If an appropriately defined Euclidean error
(between the original input and its "reconstruction" via Bayes' 
theorem) is minimised in the space of Markov chain transition
probabilities, then the familiar theories of both vector quantisers 
and self-organising maps emerge.  This approach is also used to
derive the theory of self-supervision, in which the higher layers
of a multi-layer network supervise the lower layers, even though
overall there is no external teacher.

Steve Luttrell
Adaptive Systems Theory Section
DRA, St Andrews Road, Malvern, Worcestershire, WR14 3PS, UK
email: luttrell at signal.dra.hmg.gb
Tel: +44-684-894046
Fax: +44-684-894384