Approximate Geometry Representations and Sensory Fusion
Cs. Szepesvári and A. Lõrincz
Neurocomputing
12,
267--287
(1996)
Abstract
This paper summarizes the recent advances in the theory of
self-organizing development of approximate
geometry representations based on the use of neural networks.
Part of this work is based on the theoretical approach of
(Szepesvari, 1993), which
is different from that of (Martinetz, 1993) and also is somewhat
more general. The Martinetz approach treats signals
provided by artificial
neuron-like entities whereas the present work uses the
entities of the external world as its starting point.
The relationship between the present work and the Martinetz approach
will be detailed.
We approach the problem of approximate geometry representations
by first examining the problem of sensory fusion, i.e., the problem
of fusing information from different transductors.
A straightforward solution is the simultaneous discretization of
the output of all transductors, which means the discretization of
a space defined as the product of the individual transductor output
spaces. However,
the geometry relations are defined for the 
