Kumar, R. ; Kulkarni, A. ; Jayaraman, V. K. ; Kulkarni, B. D. (2004) Symbolization assisted SVM classifier for noisy data Pattern Recognition Letters, 25 (4). pp. 495-504. ISSN 0167-8655
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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S01678...
Related URL: http://dx.doi.org/10.1016/j.patrec.2003.12.012
Abstract
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.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Elsevier Science. |
| Keywords: | Symbolization; SVM; Classification; Genetic Algorithm; Quasi-newton Algorithm |
| ID Code: | 17333 |
| Deposited On: | 16 Nov 2010 08:02 |
| Last Modified: | 06 Jun 2011 09:05 |
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