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Proceedings Paper

Comparison of support vector machines and multilayer perceptron networks in building mine classification models
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Paper Abstract

The augmentation of a currently employed baseline feature set for mine classifier design by “transform” or “moment” derived features, e.g. such as Discrete Cosine Transform and Pseudo-Zernike Moments, results in an aggregate feature set which is large in size. A “traditional” approach to this problem in the context of using multilayer perceptron(MLP) neural networks for classification consists first in the use of feature selection techniques, followed by some cross-validation based training algorithm. In this paper we contrast results obtained using the described “traditional” approach, with those obtained from using the Support Vector Machine(SVM) based framework for classifier design. The SVM approach is regarded as more attractive for large feature sets due to the optimization of a criterion in training, which is closely related to theoretical bounds on classifier generalization ability.

Paper Details

Date Published: 11 September 2003
PDF: 18 pages
Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); doi: 10.1117/12.487175
Show Author Affiliations
Martin G. Bello, ALPHATECH, Inc. (United States)
Gerald J. Dobeck, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 5089:
Detection and Remediation Technologies for Mines and Minelike Targets VIII
Russell S. Harmon; John H. Holloway Jr.; J. T. Broach, Editor(s)

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