TR available from neuroprose; learning algorithms

[Knut Moeller]

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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