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            NOVELTY DETECTION AND NEURAL NETWORK VALIDATION

                           Chris M Bishop


                    Neural Computing Research Group
                          Aston University
                       Birmingham, B4 7ET, U.K.
                    email: c.m.bishop at aston.ac.uk


          Neural Computing Research Group Report: NCRG/4289

          (Accepted for publication in IEE Proceedings)



                               Abstract

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 novel input data, that is input data which differ
significantly from the data used to train the network. In this paper 
we investigate the relationship between the degree of novelty of input 
data and the corresponding reliability of the outputs from the network.
We describe a quantitative procedure for assessing novelty, and we 
demonstrate its performance using an application involving the 
monitoring of oil flow in multi-phase pipelines.


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ftp instructions: 

% ftp archive.cis.ohio-state.edu
Name: anonymous
password: your full email address
ftp> cd pub/neuroprose
ftp> binary
ftp> get bishop.novelty.ps.Z
ftp> bye
% uncompress bishop.novelty.ps.Z
% lpr bishop.novelty.ps

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  Professor Chris M Bishop           Tel. +44 (0)21 359 3611 x4270
  Neural Computing Research Group    Fax. +44 (0)21 333 6215
  Dept. of Computer Science          c.m.bishop at aston.ac.uk
  Aston University               
  Birmingham B4 7ET, UK

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