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Unsupervised Learning

  • Berndt Müller
  • Joachim Reinhardt
Part of the Physics of Neural Networks book series (NEURAL NETWORKS)

Abstract

Although the gradient method with error back-propagation has proved to be successful at teaching multilayered neural networks to perform many tasks, it has a number of rather unrealistic aspects, especially concerning the comparison with biological nerve nets. It is particularly troublesome in this respect: that complete knowledge of the deviation of the output from the desired reaction is required to determine the adjustment even of neurons in hidden layers far separated from the output layer. It is hard to believe that such extended back-coupling mechanisms can operate in complex biological neural networks.

Keywords

Hide Layer Output Layer Input Vector Hide Neuron Output Neuron 
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 Berlin Heidelberg 1990

Authors and Affiliations

  • Berndt Müller
    • 1
  • Joachim Reinhardt
    • 2
  1. 1.Department of PhysicsDuke UniversityDurhamUSA
  2. 2.Institut für Theoretische PhysikJ.-W.-Goethe-UniversitätFrankfurt 1Fed. Rep. of Germany

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