postprint: local vs. global learning

[Patrick van der Smagt]

This paper appeared last year November at the ICNN95.

P. van der Smagt and F. Groen
	"Approximation with neural networks:
	Between local and global approximation."
In Proceedings of the 1995 International Conference on Neural Networks,
pp. II:1060-II:1064 (invited paper).

Abstract: We investigate neural network based approximation
methods. These methods depend on the locality of the basis
functions. After discussing local and global basis functions,
we propose a multi-resolution hierarchical method. The various
resolutions are stored at various levels in a tree. At the root
of the tree, a global approximation is kept; the leafs store
the learning samples themselves.  Intermediate nodes store
intermediate representations. In order to find an optimal
partitioning of the input space, self-organising maps (SOM`s)
are used. The proposed method has implementational problems
reminiscent of those encountered in many-particle simulations.
We will investigate the parallel implementation of this method,
using parallel hierarchical methods for many-particle simulations
as a starting point. 

Search for "global" on
	http://www.op.dlr.de/FF-DR-RS/Smagt/papers/