DocumentCode :
2097205
Title :
Notice of Retraction
Application of a Non-liner Classifier Model based on SVR-RBF Algorithm
Author :
Yu Chen
Author_Institution :
Dept. of Comput. & Modern Educ. Technol., Chongqing Educ. Coll., Chongqing, China
fYear :
2010
fDate :
28-31 March 2010
Firstpage :
1
Lastpage :
3
Abstract :
Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

As a branch of data mining, data classification technology has got a widely use in science, engineering, finance and other areas. The key point of the classification techniques is to construct a classifier, in this paper, a non-liner classifier model based on RBF neural network is introduced to do the data classification, compared with traditional BP neural network, it is not only avoids complicated calculation in feedforward networks, but also the local minimum problem of the gradient descent algorithm, the SVR algorithm is used to do the selection of the network center value, improved the convergence speed of the net work, at last use a example to verify the classification effect of the model, we found the SVR-RBF algorithm has a better accuracy.
Keywords :
backpropagation; data mining; gradient methods; pattern classification; radial basis function networks; regression analysis; support vector machines; SVR-RBF algorithm; backpropagation neural network; data classification technology; data mining; feedforward networks; gradient descent algorithm; nonliner classifier model; radial basis function neural networks; support vector regression; Computer science education; Data engineering; Data mining; Educational technology; Feedforward neural networks; Finance; Function approximation; Multi-layer neural network; Neural networks; Radial basis function networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Type :
conf
DOI :
10.1109/APPEEC.2010.5448575
Filename :
5448575
Link To Document :
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