scholarly journals Research and Demonstration of SVM Classification Based on Improved Gauss Kernel Function

2017 ◽  
Keyword(s):  
Kernel Function ◽  
Svm Classification ◽  
Gauss Kernel

scholarly journals Prediction Method of Equipment Degradation State Based on Improved RVM

2018 ◽  
Vol 179 ◽  
pp. 01017
Author(s):  
Lu Cheng ◽  
Wang RuiQi ◽  
Xu TingXue ◽  
Chen YuQi
Keyword(s):  
Kernel Function ◽  
Prediction Method ◽  
Relevance Vector Machine ◽  
Chaotic Sequence ◽  
Improved Method ◽  
Machine Model ◽  
Gauss Kernel ◽  
Test Parameters ◽  
Global Performance ◽  
Terminal Guidance

In order to improve the prediction accuracy of the relevance vector machine model, an improved method for equipment condition prediction is proposed. First of all, an improved kernel function of variance Gauss kernel (VGKF) is constructed to improve the global performance and generalization ability of the kernel function. Then, by using the method of selecting the number of adjacent points in the chaotic sequence local prediction method, the H-Q criterion was used to optimize the embedding dimension of the training space to avoid the blindness of subjective selection. Through the prediction example of terminal guidance radar equipment test parameters, the effectiveness and superiority of the improved RVM were verified.

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.

Combined Improved EEMD with SVM in the Application of Intellgent Fault Diagnosis

2013 ◽  
Vol 706-708 ◽  
pp. 1774-1777
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Jie Huang ◽  
Kai Chai
Keyword(s):  
Kernel Function ◽  
Classification Accuracy ◽  
Binary Classification ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Intelligent Diagnosis ◽  
Svm Classification ◽  
Development Direction ◽  
Fault State ◽  
Improved Eemd

Intelligent diagnosis is the development direction of mechahnical condition monitoring and fault diagnosis.Conbined improved EEMD with SVM in fault intelligent diagnosis is researched in this paper.To bearing normal and fault as an example,impove EEMD decomposed 9D normalized energy for characteristic vector applied to the binary classification and identification.Compared to the SVM classification accuracy using different kernel functions that is linear,polynomial,RBF and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear and polynomial kernel function is a hundred percent.Bearing normal and fault two kinds of state is completely correct apart. And the normal and fault state of the binary classification and identification using RBF and Sigmoid kernel function appeared wtong points.

Breast Tumor Recognition Based on Multiple Support Vector Machine

2012 ◽  
Vol 490-495 ◽  
pp. 252-256
Author(s):  
Hong Jiang ◽  
Xi Chen ◽  
Bai Lin Liu ◽  
Yun Qing Liu
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Breast Tumor ◽  
Recognition Accuracy ◽  
Support Vector ◽  
Gauss Kernel ◽  
Tumor Recognition ◽  
Multiple Support ◽  
Generate Vector ◽  
Reliable Basis

In order to solve unfixed size and individual difference with the breast tumor, this paper provides a method of Multi-Support Vector Machine (MSVM) for breast tumor recognition. We take Support Vector Machine (SVM) on the eight direction of bump area to generate vector classifier and select Gauss kernel function as kernel function. The breast tumor recognition accuracy can reach 97.3% when σ=30. The experiment shows that the application of MSVM in breast tumor recognition can achieve good result, and provide the reliable basis for further medical diagnosis.

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