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2013 (Vol. 5, Issue: 20)
Article Information:

Hybrid PCA/SVM Method for Recognition of Non-Stationary Time Series

Shao Qiang and Feng Chanjian
Corresponding Author:  Shao Qiang 

Key words:  Chatter gestation, pattern recognition, PCA, SVM, , ,
Vol. 5 , (20): 4857-4861
Submitted Accepted Published
September 27, 2012 November 11, 2012 May 15, 2013

A SVM (Support Vector Machine)-like framework provides a novel way to learn linear Principal Component Analysis (PCA), which is easy to be solved and can obtain the unique global solution. SVM is good at classification and PCA features are introduced into SVM. So, a new recognition method based on hybrid PCA and SVM is proposed and used for a series of experiments on non-stationary time series. The results of non-stationary time series recognition and prediction experiments are presented and show that the method proposed is effective.
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  Cite this Reference:
Shao Qiang and Feng Chanjian, 2013. Hybrid PCA/SVM Method for Recognition of Non-Stationary Time Series.  Research Journal of Applied Sciences, Engineering and Technology, 5(20): 4857-4861.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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