new paper on learning curves

[Klaus Mueller]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/klaus.lcurve.ps.Z 

The following paper is now available for copying from the Neuroprose 
repository: klaus.lcurve.ps.Z

klaus.lcurve.ps.Z 
(129075 bytes) 26 pages. 

M\"uller, K.-R., Murata, N., Finke, M., Schulten, K., Amari, S.: 
A Numerical Study on Learning Curves in Stochastic Multi-Layer 
Feed-Forward Networks

The universal asymptotic scaling laws proposed by Amari et al. 
are studied in large scale simulations using a CM5.
Small stochastic multi-layer feed-forward networks trained with
back-propagation are investigated. In the range of a
large number of training patterns $t$, the asymptotic generalization
error scales as $1/t$ as predicted. For a medium range $t$ a faster
$1/t^2$ scaling is observed.  
This effect is explained by using higher order corrections of the
likelihood expansion. It is shown for small $t$ that the scaling law
changes drastically, when the network undergoes a transition
from ineffective to effective learning.

(University of Tokyo Technical Report METR 03-95 and submitted)


* NO HARDCOPIES *


Best regards,

Klaus



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Dr. Klaus-Robert M\"uller
C/o Prof. Dr. S. Amari
Department of Mathematical Engineering
University of Tokyo
7-3-1 Hongo, Bunkyo-ku
Tokyo 113 , Japan

mail: klaus at sat.t.u-tokyo.ac.jp
Fax:   +81 - 3 - 5689 5752

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PERMANENT ADRESS:
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Dr. Klaus-Robert M\"uller
GMD First (Gesellschaft f. Mathematik und Datenverarbeitung)
Rudower Chaussee 5, 12489 Berlin
Germany

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