scholarly journals An Affinity Propagation Clustering Method Using Hybrid Kernel Function With LLE

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68892-68909 ◽  
Author(s):  
Lin Sun ◽  
Ruonan Liu ◽  
Jiucheng Xu ◽  
Shiguang Zhang ◽  
Yun Tian
Keyword(s):  
Kernel Function ◽  
Affinity Propagation ◽  
Clustering Method ◽  
Affinity Propagation Clustering ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

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

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.

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