Abstract
Original language | English |
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Place of Publication | Birmingham |
Publisher | Aston University |
Number of pages | 9 |
ISBN (Print) | NCRG/95/014 |
Publication status | Published - Mar 1995 |
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Keywords
- artificial neural networks
- information processing
- biological networks
- optimal algorithms
- correct performance
- neural network systems
- statistical pattern recognition
- principled viewpoint
- trained network
- validation
- verification
Cite this
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Neural networks: A principled perspective. / Bishop, Christopher M.
Birmingham : Aston University, 1995.Research output: Working paper › Technical report
TY - UNPB
T1 - Neural networks: A principled perspective
AU - Bishop, Christopher M.
PY - 1995/3
Y1 - 1995/3
N2 - Introductory accounts of artificial neural networks often rely for motivation on analogies with models of information processing in biological networks. One limitation of such an approach is that it offers little guidance on how to find optimal algorithms, or how to verify the correct performance of neural network systems. A central goal of this paper is to draw attention to a quite different viewpoint in which neural networks are seen as algorithms for statistical pattern recognition based on a principled, i.e. theoretically well-founded, framework. We illustrate the concept of a principled viewpoint by considering a specific issue concerned with the interpretation of the outputs of a trained network. Finally, we discuss the relevance of such an approach to the issue of the validation and verification of neural network systems.
AB - Introductory accounts of artificial neural networks often rely for motivation on analogies with models of information processing in biological networks. One limitation of such an approach is that it offers little guidance on how to find optimal algorithms, or how to verify the correct performance of neural network systems. A central goal of this paper is to draw attention to a quite different viewpoint in which neural networks are seen as algorithms for statistical pattern recognition based on a principled, i.e. theoretically well-founded, framework. We illustrate the concept of a principled viewpoint by considering a specific issue concerned with the interpretation of the outputs of a trained network. Finally, we discuss the relevance of such an approach to the issue of the validation and verification of neural network systems.
KW - artificial neural networks
KW - information processing
KW - biological networks
KW - optimal algorithms
KW - correct performance
KW - neural network systems
KW - statistical pattern recognition
KW - principled viewpoint
KW - trained network
KW - validation
KW - verification
M3 - Technical report
SN - NCRG/95/014
BT - Neural networks: A principled perspective
PB - Aston University
CY - Birmingham
ER -