Support Vector Machine Based on Hybrid Kernel Function

2012 ◽  
pp. 127-133 ◽  
Author(s):  
Xia Wu ◽  
Wanmei Tang ◽  
Xiao Wu
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Support Vector ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Wind power prediction method based on hybrid kernel function support vector machine

2017 ◽  
Vol 42 (3) ◽  
pp. 252-264 ◽  
Author(s):  
Zhongda Tian ◽  
Shujiang Li ◽  
Yanhong Wang ◽  
Xiangdong Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Kernel Function ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

The prediction accuracy of wind power affects the operation cost of the power grid, which is a direct result of the supply and demand balance of the grid. Therefore, how to improve the prediction accuracy of wind power is very important. Considering the prediction accuracy of current prediction methods is not high, a wind power prediction method based on a hybrid kernel function support vector machine is proposed. On the basis of the exhibited characteristics of different kernel functions, the hybrid kernel function is a linear combination of the radial basis function and the polynomial kernel function. The hybrid kernel function is selected as the kernel function of support vector machine. The global kernel function is used to fit the correlation of the distant sample data, while the partial kernel function is used to fit the correlation of the data in neighboring fields. The generalization performance of the support vector machine model is improved. At the same time, an improved particle swarm optimization algorithm is introduced to determine the optimal parameters of the hybrid kernel function and support vector machine prediction model. Finally, the built prediction model is used to predict the wind power. The simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

Stock Price Index Prediction Based on Improved SVM

2011 ◽  
Vol 267 ◽  
pp. 468-471
Author(s):  
Jin Yan Shi ◽  
Xue Li ◽  
Yan Xi Li
Keyword(s):  
Kernel Function ◽  
Stock Price ◽  
Price Index ◽  
Kernel Functions ◽  
Classification Model ◽  
Support Vector ◽  
Theoretical Advantage ◽  
Stock Price Index ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Accurate stock price predicting is a key problem to the financial field. Comparing with the traditional stock price predicting models such as GARCH models and neural networks, the theoretical advantage of applying support vector machine (SVM) to stock price predicting highly depends on solving the problem of kernel function construction and parameter optimization. For the effect of the kernel function in the SVM classification model, a hybrid kernel function is presented. In order to optimize and adjust the important parameters during the process of building the hybrid kernel function, an improved particle swarm optimization which has better global search ability is used. Experimental results about stock price index predicting show that this method has higher prediction accuracy compared with the traditional kernel functions.

Parameters optimization of combined kernel function for support vector machine

2013 ◽  
Vol 33 (5) ◽  
pp. 1321-1323 ◽  
Author(s):  
Junbao GENG ◽  
Linkai SUN ◽  
Shixue CHEN
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Parameters Optimization ◽  
Support Vector

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction

2021 ◽  
Vol 231 ◽  
pp. 107398
Author(s):  
Zhong Yuan ◽  
Hongmei Chen ◽  
Xiaoling Yang ◽  
Tianrui Li ◽  
Keyu Liu
Keyword(s):  
Kernel Function ◽  
Attribute Reduction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

2021 ◽  
Author(s):  
Jaeseung Baek ◽  
Taha J. Alhindi ◽  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Seongho Seo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Kernel Function ◽  
Dynamic Time Warping ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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