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A paper, with principal audience statisticians, entitled

	Statistical Aspects of Neural Networks

is available by anonymous ftp from 

	markov.stats.ox.ac.uk (192.76.20.1 or 129.67.1.190)

at pub/neural/papers/ripley.ps.Z (336kB), with abstract ripley.abstract as
follows:

Neural networks have been a much-publicized topic of research in the
last five years, and are now beginning to be used in a wide range of
subject areas traditionally thought by statisticians to be their
domain. This paper explores the basic ideas of neural networks from the
point of view of a statistician, and compares some of their
applications with those of traditional and modern methods of statistics
and pattern recognition.

Neural networks are mainly used as non-linear approximations to
multivariable functions or as classifiers.  They are non-parametric in
character in that no subject-domain knowledge is incorporated in the
modelling process, and the parameters are estimated using algorithms
which at least in principle can be computed on loosely-coupled parallel
computers.  We argue that the modelling-based approach traditional in
statistics and pattern recognition can be at least as effective, and
often more so. This is illustrated by data on the areas in Zimbabwe
environmentally suitable for Tsetse flies.


Invited lectures for SemStat (S\'eminaire Europ\'een de
Statistique), Sandbjerg, Denmark, 25-30 April 1992. To appear in the
proceedings to be published by Chapman & Hall in January 1993.

.----------------------------------------------------.
|      Prof. Brian D. Ripley                         |
|      Dept. of Statistics,  University of Oxford,   |
|      1 South Parks Road,   Oxford OX1 3TG, UK      |
|                                                    |
|      ripley@uk.ac.ox.stats (JANET)                 |
|      ripley@stats.ox.ac.uk (Internet)              |
`----------------------------------------------------'