Allinson, N. M. and Kolcz, A. (1977) A principled approach to N-tuple recognition systems. In: IEE Colloquium on Pattern Recognition, 26 February 1997, London.

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Abstract

The idea of n-tuple sampling as a basis for pattern recognition, as proposed by Bledsoe and Browning (1959), remains a viable approach to a range of pattern classification tasks especially where speed of learning is of importance. The formal relationship between n-tuple neural networks and more mainstream network paradigms, such as radial basis function networks, and classical nonparametric pattern classifiers, such as kernel estimation, is considered, and it is described how the classic n-tuple recogniser and the n-tuple regression network form differing approximations in the classification process

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	The idea of n-tuple sampling as a basis for pattern recognition, as proposed by Bledsoe and Browning (1959), remains a viable approach to a range of pattern classification tasks especially where speed of learning is of importance. The formal relationship between n-tuple neural networks and more mainstream network paradigms, such as radial basis function networks, and classical nonparametric pattern classifiers, such as kernel estimation, is considered, and it is described how the classic n-tuple recogniser and the n-tuple regression network form differing approximations in the classification process
Keywords:	N-tuple neural networks, pattern classification, kernel estimation, learning speed, pattern recognition, radial basis function networks
Subjects:	G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Tammie Farley
Date Deposited:	20 Apr 2012 18:28
Last Modified:	13 Mar 2013 09:06
URI:	http://eprints.lincoln.ac.uk/id/eprint/5081

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