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A model for a multi-class classification machine

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  • Rau, Albrecht
  • Nadal, Jean-Pierre

Abstract

We consider the properties of multi-class neural networks, where each neuron can be in several different states. The motivations for considering such systems are manifold. In image processing for example, the different states correspond to the different grey tone levels. Another multi-class classification task implemented on a feed-forward network is the analysis of DNA sequences or the prediction of the secondary structure of proteins from the sequence of amino acids.

Suggested Citation

  • Rau, Albrecht & Nadal, Jean-Pierre, 1992. "A model for a multi-class classification machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 185(1), pages 428-432.
    • Handle: RePEc:eee:phsmap:v:185:y:1992:i:1:p:428-432
    DOI: 10.1016/0378-4371(92)90484-8
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    Cited by:

    1. Darío Ramos-López & Ana D. Maldonado, 2021. "Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks," Mathematics, MDPI, vol. 9(2), pages 1-15, January.
    2. Gzyl, Henryk & ter Horst, Enrique & Molina, German, 2015. "Application of the method of maximum entropy in the mean to classification problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 101-108.

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