RTIS 2016: Lecture Notes in Real-Time Intelligent Systems pp 189-195 | Cite as
Demonstration of SVM Classification Based on Improved Gauss Kernel Function
Abstract
This article carries on the analysis in view of the Gaussian kernel function among many kernel function support vector machine, which explains the scope of application of Gaussian kernel function and design discriminant algorithm whether sample data for the Gaussian distribution. Select the square of Hilbert space as standards of separability measure data. The design maximizes evaluation function based on the classification of the Gaussian kernel function parameter intervals. The process of kernel function parameter optimization algorithm is designed. The results prove that - It has the very high practical value that Gaussian kernel function parameter optimization algorithm which is designed in this paper. The kernel function parameters λ = 0.6; comparatively analyzed Support vector machine classification results when λ = 0.1, 0.2,…,0.9; Verify the superiority of the parameter optimization algorithm.
Keywords
Support vector machine Gaussian kernel function Hilbert space Classification interval Parameter optimizationNotes
Acknowledgments
This work was supported by National Key Research and Development Program (no. 2016YFB0601403), the National Natural Science Foundation of China (no. 51504080), and the National Natural Science Foundation of Hebei Education Department (no. QN2016088).
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