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.
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