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Symbolization assisted SVM classifier for noisy data
R. Kumar, A. Kulkarni, , B.D. Kulkarni
Published in Elsevier
Volume: 25
Issue: 4
Pages: 495 - 504
The paper reports on the robust pattern classification of experimental data using a combined approach of symbolization followed by support vector machine (SVM) classification. Symbolization of data removes unwanted features such as noise whereas SVM provides the classification. The SVM parameters are tuned on-line using a genetic-quasi-Newton algorithm. Benchmark examples illustrate the proposed approach. © 2004 Elsevier B.V. All rights reserved.
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Published in Elsevier
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