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A neural network expert system with confidence measurements

  • 10. Neural Networks
  • Conference paper
  • First Online:
Uncertainty in Knowledge Bases (IPMU 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 521))

Abstract

We show two ways to attach confidence measurements to MACIE, a connectionist expert system model. One technique involves multiple generation of neural network knowledge bases and parallel execution of expert system inference engines. Required modifications for forward and backward chaining and explanations by If-Then rules with confidence measurements are given.

In the second approach we show that inferences made by the MACIE connectionist expert system with partial information have the same confidence as inferences made with full information under certain standard probability models, including distribution-free Valiant-style learning.

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References

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Author information

Authors and Affiliations

  1. ICANN, 51 Fenno St., 02138, Cambridge, MA, USA

    Stephen I. Gallant

  2. Department of Computer & Information Science, Ibaraki University, Nakanarusawa, Hitachi-shi, 316, Ibaraki, Japan

    Yoichi Hayashi

Authors
  1. Stephen I. Gallant
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  2. Yoichi Hayashi
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Editor information

Bernadette Bouchon-Meunier Ronald R. Yager Lotfi A. Zadeh

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© 1991 Springer-Verlag Berlin Heidelberg

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Gallant, S.I., Hayashi, Y. (1991). A neural network expert system with confidence measurements. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028143

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  • DOI: https://doi.org/10.1007/BFb0028143

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54346-6

  • Online ISBN: 978-3-540-47580-4

  • eBook Packages: Springer Book Archive

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