Comparison of supervised and unsupervised discriminator-based logic neural networks

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Comparison of supervised and unsupervised discriminator-based logic neural networks

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Published

A self-organising discriminator-based logic neural network is compared to the similarly-structured supervised WISARD neural network on the basis of their performance in a pattern recognition task. The self-organising system is shown to possess a superior performance in learning environments where the training patterns have a high degree of variability.

Inspec keywords: unsupervised learning; pattern recognition; self-organising feature maps

Other keywords: supervised WISARD neural network; discriminator-based logic neural networks; training pattern; supervised neural network; self-organising neural network; pattern recognition task; learning environments

Subjects: Pattern recognition; Adaptive system theory; Neural nets (theory)

References

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      • G. Tambouratzis , T.J. Stonham , I. Aleksander , J. Taylor . (1992) Implementing hard self-organisation tasks using logical neural networks, Artificial neural networks-II.
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      • I. Aleksander , W.V. Thomas , P.A. Bowden . WISARD: A radical step forward in image recognition. Sensor Rev. , 120 - 124
    3. 3)
      • I. Aleksander , H. Morton . (1990) An introduction to neural computing.
    4. 4)
      • Tambouratzis, G., Stonham, T.J.: `Data clustering in complex pattern spaces using a self-organising logicneural network', Proc. 1993 Weightless Neural Network Workshop, 6–7 April 1993, York, UK, p. 70–75.
    5. 5)
      • G. Tambouratzis , T.J. Stonham . Evaluating the topology-preservation capabilities of a self-organisinglogical neural network. Pattern Recog. Lett. , 11 , 927 - 934
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      • T. Kohonen . (1989) Self-organisation and associative memory.
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