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- Newsgroups: comp.ai.neural-nets
- Path: sparky!uunet!europa.asd.contel.com!darwin.sura.net!mojo.eng.umd.edu!disney.src.umd.edu!delliott
- From: delliott@src.umd.edu (David L. Elliott)
- Subject: Re: Neural-nets and (vs.) Statistical Methods
- Message-ID: <1992Aug26.223430.28354@src.umd.edu>
- Summary: Repost of info from Brian Ripley
- Sender: news@src.umd.edu (C-News)
- Organization: College of Engineering, University of Maryland, College Park
- References: <1992Aug26.165944.28843@cco.caltech.edu>
- Distribution: Comp.ai.neural-nets
- Date: Wed, 26 Aug 1992 22:34:30 GMT
- Lines: 70
-
- Repost of info from Brian Ripley about his article on ANN vs Statistical methods
- ....................................
- From: ripley@vax.oxford.ac.uk
- Newsgroups: comp.ai.neural-nets
- Subject: Paper available: `Statistical Aspects of Neural Networks'
- Message-ID: <1992Aug3.150644.7990@vax.oxford.ac.uk>
- Date: 3 Aug 92 14:06:44 GMT
- Organization: Oxford University VAXcluster
- Lines: 42
-
- 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/S/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) |
- `----------------------------------------------------'
-
-
- From disney.src.umd.edu!mojo.eng.umd.edu!darwin.sura.net!europa.asd.contel.com!uunet!mcsun!uknet!ox-prg!oxuniv!ripley Thu Aug 6 15:30:57 EDT 1992
- Article: 7291 of comp.ai.neural-nets:
- Path: disney.src.umd.edu!mojo.eng.umd.edu!darwin.sura.net!europa.asd.contel.com!uunet!mcsun!uknet!ox-prg!oxuniv!ripley
- From: ripley@vax.oxford.ac.uk
- Newsgroups: comp.ai.neural-nets
- Subject: Correct reference for `Statistical Aspects of Neural Networks'
- Message-ID: <1992Aug3.151055.7993@vax.oxford.ac.uk>
- Date: 3 Aug 92 14:10:55 GMT
- Organization: Oxford University VAXcluster
- Lines: 3
-
- My apologies, the paper is at: pub/neural/papers/ripley.ps.Z
-
- Brian Ripley
-
-
-