The effect of a non-exhaustively defined set of classes on neural network classifications
The effect of a non-exhaustively defined set of classes on neural network classifications
Freedom from assumptions about the data set used is one attraction of neural network classifiers. However, neural network classification is not assumption-free. It is typically assumed that the set of classes has been defined exhaustively. If this assumption is unsatisfied, cases of an untrained class will be present and commissioned into the set of trained classes to the detriment of classification accuracy, for both hard and soft classifications. This is illustrated with MLP and RBF neural networks together with suggestions of how to reduce the problem for both hard and soft image classifications
geophysical signal processing, image classification, multilayer perceptrons, radial basis, function networks, remote sensing, mlp neural networks, rbf neural networks, classification accuracy, image classifications, neural network classifications, nonexhaustively defined classes, trained classes, untrained class
2731-2733
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
2001
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Foody, G.M.
(2001)
The effect of a non-exhaustively defined set of classes on neural network classifications.
In Geoscience and Remote Sensing Symposium, IGARSS '01. IEEE 2001 International, Sydney, Australia, 9-13 Jul 2001.
IEEE.
.
(doi:10.1109/IGARSS.2001.978144).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Freedom from assumptions about the data set used is one attraction of neural network classifiers. However, neural network classification is not assumption-free. It is typically assumed that the set of classes has been defined exhaustively. If this assumption is unsatisfied, cases of an untrained class will be present and commissioned into the set of trained classes to the detriment of classification accuracy, for both hard and soft classifications. This is illustrated with MLP and RBF neural networks together with suggestions of how to reduce the problem for both hard and soft image classifications
This record has no associated files available for download.
More information
Published date: 2001
Venue - Dates:
conference; 2001-07-09; 2001-07-13, 2001-07-09 - 2001-07-13
Keywords:
geophysical signal processing, image classification, multilayer perceptrons, radial basis, function networks, remote sensing, mlp neural networks, rbf neural networks, classification accuracy, image classifications, neural network classifications, nonexhaustively defined classes, trained classes, untrained class
Identifiers
Local EPrints ID: 15216
URI: http://eprints.soton.ac.uk/id/eprint/15216
PURE UUID: c6f5feac-5cf7-4102-8c87-c712226219aa
Catalogue record
Date deposited: 12 Apr 2005
Last modified: 15 Mar 2024 05:36
Export record
Altmetrics
Contributors
Author:
G.M. Foody
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics