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Proceedings Paper

Consequences of preprocessing feature data for support vector machines
Author(s): David J. Gorsich; Robert E. Karlsen; Grant R. Gerhart
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Paper Abstract

Support vector machines are classification algorithms based on quadratic programming that have been found to give excellent classification results on problems such as discriminating targets form backgrounds. A key capability of these algorithms is that they do not require a preprocessing step to determine feature vectors, yet preprocessing is still an important step in the classification process. We discuss the effects of preprocessing feature data on the support vectors and the classification results of support vector machines. We first give a short introduction to support vector machines. Several methods to preprocess the data before being sent to the support vector machine are discussed. Then the algorithm is applied a set of second- order stochastic textures defined by their covariance structure. The effect on the classification rate is then determined.

Paper Details

Date Published: 22 October 2001
PDF: 8 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445383
Show Author Affiliations
David J. Gorsich, U.S. Army Tank-Automotive and Armaments Command (United States)
Robert E. Karlsen, U.S. Army Tank-Automotive and Armaments Command (United States)
Grant R. Gerhart, U.S. Army Tank-Automotive and Armaments Command (United States)


Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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