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[Michael Biehl]

FTP-host: archive.cis.ohio-state.edu
FTP-filename:  /pub/neuroprose/biehl.unsupervised.ps.Z

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The following paper has been placed in the Neuroprose archive
(see above for ftp-host)  in file 
 biehl.unsupervised.ps.Z   (8 pages of output)

email address of author: biehl at physik.uni-wuerzburg.de 

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  "An exactly solvable model of unsupervised learning"
       by  Michael Biehl 

Abstract:
A model for unsupervised learning from N-dimensional data is
studied. Random training examples are drawn such that the
distribution of their overlaps with a vector B is a mixture of
two Gaussians of unit width and a separation rho.  A student
vector is generated by an online algorithm, using each example
only once. The evolution of its overlap with B can be calculated
exactly in the thermodynamic limit (infinite N). As a specific
example Oja's rule is investigated.   Its dynamics and approach
to the stationary solution are solved for both a constant and an
optimally chosen time-dependent learning rate.

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