Topology Learning Solved by Extended Objects: A Neural Network Model View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1993

AUTHORS

Csaba Szepesvári , András Lörincz

ABSTRACT

We describe a self-organizing artificial neural network architecture that simultaneously creates (1) local filters with competitive learning, (2) a representation of the topology of the external world with Hebbian learning, and introduces (3) Kohonen type cooperative neighbour training through the self-developed connections. Such a network is capable of building up a 3-dimensional topology from two 2-dimensional images — similar to the working of the human eye — and it shows increased adaptivity. More... »

PAGES

678-678

Book

TITLE

ICANN ’93

ISBN

978-3-540-19839-0
978-1-4471-2063-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4471-2063-6_186

DOI

http://dx.doi.org/10.1007/978-1-4471-2063-6_186

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031618010


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