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A multi-interacting perceptron model with continuous outputs

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  • de Almeida, R.M.C.
  • Botelho, E.

Abstract

We consider learning and generalization of real functions by a multi-interacting feed-forward network model with continuous outputs with invertible transfer functions. The expansion in different multi-interacting orders provides a classification for the functions to be learnt and suggests the learning rules, that reduce to the Hebb-learning rule only for the second order, linear perceptron. The over-sophistication problem is straightforwardly overcome by a natural cutoff in the multi-interacting synapses: the student is able to learn the architecture of the target rule, that is, the simpler a rule is the faster the multi-interacting perception may learn. Simulation results are in excellent agreement with analytical calculations.

Suggested Citation

  • de Almeida, R.M.C. & Botelho, E., 1997. "A multi-interacting perceptron model with continuous outputs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 242(1), pages 27-37.
    • Handle: RePEc:eee:phsmap:v:242:y:1997:i:1:p:27-37
    DOI: 10.1016/S0378-4371(97)00188-X
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    Cited by:

    1. Botelho, E, 1998. "Learning the reversed-wedge problem using a multi-interacting perceptron with correlated weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 253(1), pages 323-332.

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