A New Pattern Recognition Method Based on Nonlinear Support Vector Machine

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The methods based on empirical risk minimization are often applied to pattern recognition. But the predictive validities of these methods are not perfect with small sample data. This paper introduces a nonlinear support vector machine (SVM) based on structural risk minimization which can obtain global optimization other than local one and better generalization. The nonlinear SVM is with robust predictive performance, especially in small samples. The nonlinear SVM is robust and may obtain higher recognition rates.
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Pattern Recognition; Support Vector Machine; Kernel Function

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