Symbolization assisted SVM classifier for noisy data

摘要

The paper reports on the robust pattern classification of experimental data using a combined approach of sym-bolization 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.