Paper Announcement

[Sung-Bae Cho]

Feedforward Neural Network Architectures for
Complex Classification Problems 

To appear in the Fuzzy Systems & AI journal (Romanian Academia Publishing
House). The idea of this paper was presented at the 2nd International
Conference on Fuzzy Logic & Neural Networks, Iizuka-92.

Sung-Bae Cho (sbcho at gorai.kaist.ac.kr) and Jin H. Kim

Center for Artificial Intelligence Research and Computer Science Department
Korea Advanced Institute of Science and Technology
373-1, Koosung-dong, Yoosung-ku, Taejeon 305-701, Republic of Korea

Abstract

This paper presents two neural network design strategies for incorporating
a priori knowledge about a given problem into the feedforward neural
networks. These strategies aim at obtaining tractability and reliability
for solving complex classification problems by neural networks.
The first type strategy based on multistage scheme decomposes the problem
into manageable ones for reducing the complexity of the problem,
and the second type strategy on multiple network scheme combines incomplete
decisions from several copies of networks for reliable decision-making.
A preliminary experiment of recognizing on-line handwriting characters
confirms the superiority relative to a single large neural network classifier.

Key words: neural network architecture design, multistage neural network,
multiple neural networks, synthesis method, voting method, expert judgement,
handwriting character recognition

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Now available in the neuroprose archive:
  archive.cis.ohio-state.edu (128.146.8.52)
  pub/neuroprose directory
under the file name
  sbcho.nn_architects.ps.Z
(compressed PostScript).