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A Scalable Neural Architecture Combining Unsupervised and Suggestive Learning

  • R. B. Lambert
  • W. P. Cockshott
  • R. J. Fryer
Conference paper

Abstract

Multi-layered perceptions are, in theory, capable of solving a wide range of problems. However, as the scale of many problems is increased, or requirements change, multi-layered perceptrons fail to learn or become impractical to implement. Self-organizing networks are not so limited by scale, but require a-priori information, typically in the form of preset weights or suitable control parameters to achieve a good categorization of a data set.

Based on research into the behaviour of biological neurons during learning, a new self-organizing neural network has been devised. Moving away from the traditional McCulloch and Pitts model, each neuron stores several independent patterns, each capable of initiating a neuron output. By structuring such neurons into a network, a rapid and equal distribution of data across competitive nodes is possible.

This paper introduces the new network, known as a Master-Slave architecture, and learning paradigm. By using competitive and suggestive learning, inputs are distributed across all available classification units, without the need for a-priori knowledge. Two experiments are described, highlighting the potential of the master-slave architecture as a building block for larger networks.

Keywords

Weight Vector Network Input Categorization Node Matching Node Competitive Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag/Wien 1993

Authors and Affiliations

  • R. B. Lambert
    • 1
  • W. P. Cockshott
    • 1
  • R. J. Fryer
    • 1
  1. 1.Dept. Computer ScienceUniversity of StrathclydeGlasgowScotland

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