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New Mixed Kernel Functions of SVM Used in Pattern Recognition Cover

New Mixed Kernel Functions of SVM Used in Pattern Recognition

By: Hao Huanrui  
Open Access
|Oct 2016

Abstract

The pattern analysis technology based on kernel methods is a new technology, which combines good performance and strict theory. With support vector machine, pattern analysis is easy and fast. But the existing kernel function fits the requirement. In the paper, we explore the new mixed kernel functions which are mixed with Gaussian and Wavelet function, Gaussian and Polynomial kernel function. With the new mixed kernel functions, we check different parameters. The results shows that the new mixed kernel functions have good time efficiency and accuracy. In image recognition we used SVM with two mixed kernel functions, the mixed kernel function of Gaussian and Wavelet function are suitable for more states.

DOI: https://doi.org/10.1515/cait-2016-0047 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 5 - 14
Published on: Oct 20, 2016
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Hao Huanrui, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.