Consequences of preprocessing feature data for support vector machines

David J. Gorsich; Robert E. Karlsen; Grant R. Gerhart

doi:10.1117/12.445383

22 October 2001 Consequences of preprocessing feature data for support vector machines

David J. Gorsich, Robert E. Karlsen, Grant R. Gerhart

Proceedings Volume 4379, Automatic Target Recognition XI; (2001) https://doi.org/10.1117/12.445383
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

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David J. Gorsich, Robert E. Karlsen, and Grant R. Gerhart "Consequences of preprocessing feature data for support vector machines", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); https://doi.org/10.1117/12.445383

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KEYWORDS

Image classification

Stochastic processes

Matrices

Computer programming

Detection and tracking algorithms

Linear filtering

Nonlinear filtering

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