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A Prediction of Speech Volume Based on Support Vector Machine
Han Huimin * #
Automation School of Beijing University of Posts and Telecommunications
*Correspondence author
#Submitted by
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Funding: none
Opened online:16 April 2007
Accepted by: none
Citation: Han Huimin.A Prediction of Speech Volume Based on Support Vector Machine[OL]. [16 April 2007] http://en.paper.edu.cn/en_releasepaper/content/12231
 
 
Support Vector Machine (SVM) is a new tool in virtue of optimization to resolve machine learning problems, and a new technique in DataMining area. Support Vector Machine (SVM) is a new common learning approach developed by Vapnik and others on basis of statistical learning theory in recent years. It is based on the structural risk minimization criteria. This paper presents the principle and algorithm of SVR (Support Vector Regression). The application of SVR to the analysis and prediction of speech volume increases efficiency. The satisfied result of emulation shows that the method of SVR is advanced and with high accuracy and good generalization.
Keywords:Support Vector Regression, Speech Volume, prediction
 
 
 

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