Hardware Design of Gaussian Kernel Function for Non-Linear SVM Classification

2021 ◽  
Author(s):  
Yuanfa Wang ◽  
Yu Pang ◽  
Huan Huang ◽  
Qianneng Zhou ◽  
Jiasai Luo
Keyword(s):  
Kernel Function ◽  
Hardware Design ◽  
Gaussian Kernel ◽  
Svm Classification ◽  
Gaussian Kernel Function ◽  
Non Linear ◽  
Linear Svm

Adaptive Channel Balancing Algorithm Based on 2-D Gaussian Kernel Function

2021 ◽  
pp. 1-5
Author(s):  
Leilei Xu ◽  
Peng Liu ◽  
Bingji Zhao ◽  
Qingjun Zhang ◽  
Yaqiu Jin
Keyword(s):  
Kernel Function ◽  
Gaussian Kernel ◽  
Gaussian Kernel Function

scholarly journals Adaptive Image Mismatch Removal With Vector Field Interpolation Based on Improved Regularization and Gaussian Kernel Function

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 55599-55613 ◽  
Author(s):  
Yongjun Zhang ◽  
Xunwei Xie ◽  
Xiang Wang ◽  
Yansheng Li ◽  
Xiao Ling
Keyword(s):  
Vector Field ◽  
Kernel Function ◽  
Gaussian Kernel ◽  
Gaussian Kernel Function

Kernel Parameter Selection for SVM Classification

2011 ◽  
pp. 44-55
Author(s):  
Manju Bala ◽  
R. K. Agrawal
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Classification Accuracy ◽  
Parameter Selection ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classification ◽  
Kernel Parameter ◽  
Benchmark Datasets

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

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