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Archive-name: ai/neural-nets/fox-decomp/1991-06-14
Archive: cheops.cis.ohio-state.edu:/pub/neuroprose/fox.decomp.* [128.146.8.62]
Original-posting-by: moeller@kiti.informatik.uni-bonn.de
Original-subject: [Knut Moeller: TR available from neuroprose; learning algorithms]
Reposted-by: emv@msen.com (Edward Vielmetti, MSEN)


------- Forwarded Message

Date: Thu, 13 Jun 91 09:50:34 +0200
From: Knut Moeller <moeller@kiti.informatik.uni-bonn.de>
Message-Id: <9106130750.AA01054@kiti.>
Subject: TR available from neuroprose; learning algorithms

The following report is now available from the neuroprose archive:

	LEARNING BY ERROR-DRIVEN DECOMPOSITION
   D.Fox  V.Heinze  K.Moeller  S.Thrun  G.Veenker  (6pp.)

Abstract: In this paper we describe a new selforganizing decomposition
technique for learning high-dimensional mappings. Problem
decomposition is performed in an error-driven manner, such that the
resulting subtasks (patches) are equally well approximated.  Our
method combines an unsupervised learning scheme (Feature Maps
[Koh84]) with a nonlinear approximator (Backpropagation
[RHW86]). The resulting learning system is more stable and
effective in changing environments than plain backpropagation and much
more powerful than extended feature maps as proposed by
[RMW89]. Extensions of our method give rise to active
exploration strategies for autonomous agents facing unknown
environments.  
The appropriateness of this technique is demonstrated with an example
from mathematical function approximation.





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-- comp.archives file verification
cheops.cis.ohio-state.edu
-rw-r--r--  1 3169     274        745782 Jun 10 10:10 /pub/neuroprose/fox.decomp.ps.Z
found fox-decomp ok
cheops.cis.ohio-state.edu:/pub/neuroprose/fox.decomp.*