Paper on Neural Network Classification Available

[Dr. Michael Wong]

FTP-host: physics.ust.hk
FTP-file: pub/kymwong/nips95.ps.gz

The following paper, submitted to the Theory session of NIPS-95, 
is now available via anonymous FTP. (8 pages long)
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           Neural Network Classification of Non-Uniform Data

                    K. Y. Michael Wong and H. C.Lau,
Department of Physics, The Hong Kong University of Science and Technology,
                  Clear Water Bay, Kowloon, Hong Kong.
      E-mail address: phkywong at usthk.ust.hk, phhclau at usthk.ust.hk


                               ABSTRACT

We consider a model of non-uniform data, which resembles typical data for 
system faults in diagnostic classification tasks. Pre-processing the data 
for feature extraction and dimensionality reduction improves the 
performance of neural network classifiers, in terms of the number of 
training examples required for good generalization. This result supports 
the use of hybrid expert systems in which feature extraction techniques 
such as classification trees are used to build a pre-processing layer 
for neural network classifiers.

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