Modular, Hierarchical, and Geometrical Neural Networks (bibtex)
by Horst Bischof
Abstract:
In order to apply neural networks to large scale, real world tasks, several obstacles have to be overcome. One main deficiency is long learning time. A closer look at the brain reveals that the topology of the brain is considerably different from current neural network models. We show that modular and hierarchical toplogies (also common in the brain) offer a potential solution to speed up learning. To build modular and hierarchical network topologies, knowledge from conventional
Reference:
Modular, Hierarchical, and Geometrical Neural Networks (Horst Bischof), Technical report, PRIP, TU Wien, 1991.
Bibtex Entry:
@TechReport{TR009,
  author =	 "Horst Bischof",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-009",
  title =	 "Modular, {H}ierarchical, and {G}eometrical {N}eural
                  {N}etworks",
  year =	 "1991",
  abstract =	 "In order to apply neural networks to large scale,
                  real world tasks, several obstacles have to be
                  overcome. One main deficiency is long learning
                  time. A closer look at the brain reveals that the
                  topology of the brain is considerably different from
                  current neural network models. We show that modular
                  and hierarchical toplogies (also common in the
                  brain) offer a potential solution to speed up
                  learning. To build modular and hierarchical network
                  topologies, knowledge from conventional",
  url =		 "ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr9.ps.gz",
}
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