Least Squares Support Vector Machine on Gaussian Wavelet Kernel Function Set

2006 ◽  
pp. 936-941 ◽  
Author(s):  
Fangfang Wu ◽  
Yinliang Zhao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Kernel Function ◽  
Support Vector ◽  
Wavelet Kernel Function

Research on Nonlinear Modeling Method of Support Vector Machine with Wavelet Derivation Kernel Function

2014 ◽  
Vol 687-691 ◽  
pp. 1408-1411
Author(s):  
Ping An Wang ◽  
Xu Sheng Gan ◽  
Wen Ming Gao
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Nonlinear Modeling ◽  
Nonlinear Function ◽  
Modeling Method ◽  
Good Effect ◽  
Support Vector ◽  
Satisfactory Approximation ◽  
Wavelet Kernel Function ◽  
Nonlinear Function Approximation

The model capability of Support Vector Machine (SVM) relies on the selection of kernel function. To obtain a better application modeling of SVM, the wavelet kernel function that satisfies Merce condition is introduced to use the kernel function of SVM, achieving a good effect. In the paper, on the basis of wavelet kernel function, a wavelet derivation kernel function is proposed in the application of SVM for higher accuracy. An actual example on nonlinear function approximation shows that SVM regression model has a satisfactory approximation effect, and also support an effective nonlinear modeling method.

Anomaly Intrusion Detection Based on Support Vector Machine with Mexico Hat Wavelet Kernel Function

2014 ◽  
Vol 687-691 ◽  
pp. 3897-3900 ◽  
Author(s):  
Ping An Wang ◽  
Xu Sheng Gan ◽  
Deng Kai Yao
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Kernel Function ◽  
Great Influence ◽  
Model Performance ◽  
Necessary Condition ◽  
Support Vector ◽  
Rbf Kernel ◽  
Wavelet Kernel Function ◽  
Anomaly Intrusion Detection

The selection of kernel function in Support Vector Machine (SVM) has a great influence on the model performance. In the paper, Mexico hat wavelet kernel is introduced to employ the kernel function of SVM, and theoretically it has be prove that, Mexico hat wavelet kernel satisfies the Merce condition, that is the necessary condition as the kernel function of SVM. Simulation on the anomaly detection shows that the capability of SVM based on Mexico hat wavelet kernel is better than that of SVM based on RBF kernel with a satisfactory result for anomaly intrusion detection.

Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach

2019 ◽  
Vol 233-234 ◽  
pp. 196-207 ◽  
Author(s):  
Bangzhu Zhu ◽  
Shunxin Ye ◽  
Minxing Jiang ◽  
Ping Wang ◽  
Zhanchi Wu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Kernel Function ◽  
Function Approach ◽  
Support Vector ◽  
Carbon Intensity ◽  
Carbon Intensity Target

scholarly journals Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

2021 ◽  
Vol 50 (2) ◽  
pp. 319-331
Author(s):  
Wenlu Ma ◽  
Han Liu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Least Squares ◽  
Kernel Function ◽  
Large Scale ◽  
Kernel Learning ◽  
Selection Strategy ◽  
Support Vector ◽  
Power Load ◽  
Sparse Samples

Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

scholarly journals A rolling bearing fault diagnosis method based on LSSVM

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401989956
Author(s):  
Xuejin Gao ◽  
Hongfei Wei ◽  
Tianyao Li ◽  
Guanglu Yang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Kernel Function ◽  
Dimensional Space ◽  
Rolling Bearing ◽  
Classification Model ◽  
Support Vector ◽  
Bearing Fault ◽  
Diagnosis Method

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning
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