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As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to SVM for feature extraction. Then PSO Algorithm is adopted to optimization of these parameters in SVM. The novel time series...
The support vector machine classification method is used for source enumeration; i.e., estimating the number of sources contributed in generation of the signals received by the sensors of a passive array. The main motivation comes from the problems where the data model is not completely known, or the model is subject to some changes due to real environmental conditions, (i.e., the case of data model...
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