ICANN ’93 pp 789-794 | Cite as

Novelty Detection and Neural Network Validation

  • C. M. Bishop


One of the key factors limiting the use of neural networks in many industrial applications has been the difficulty of demonstrating that a trained network will continue to generate reliable outputs once it is in routine use. An important potential source of errors arises from input data which differs significantly from that used to train the network. In this paper we investigate the relation between the degree of novelty of input data and the corresponding reliability of the output data. We provide a quantitative procedure for measuring novelty, and we demonstrate its performance using an application involving the monitoring of oil flow in multi-phase pipelines.


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  1. [1]
    Silverman B W (1986) Density Estimation, Chapman and Hall, New York.MATHGoogle Scholar
  2. [2]
    Bishop, C.M. and James, G.D. (1993) Monitoring of Multiphase Flows using Dual-Energy Gamma Densitometry and Neural Networks, Nuclear Instruments and Methods in Physics Research A327 580–593.Google Scholar

Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • C. M. Bishop
    • 1
  1. 1.Applied Neurocomputing Centre, AEA TechnologyHarwell LaboratoryOxfordshireUK

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