DocumentCode :
575810
Title :
Reduce the samples for SVM based on Euclidean distance
Author :
Hongle, Du ; Qiong, Lu ; Jing, Cao
Author_Institution :
Dept. of Comput. Sci., Shangluo Univ., Shangluo, China
Volume :
1
fYear :
2012
fDate :
20-21 Oct. 2012
Firstpage :
72
Lastpage :
75
Abstract :
Propose a reduced sample algorithm for SVM based on Euclidean distance according to analysis the distribution feature of the support vectors. Firstly, this method pre-defines the Quasi-classification hypeplane. Then select the boundary samples according to the Euclidean distance from one sample point to the Quasi-classification hypeplane and get new training dataset. Because the new training dataset is the subset of the original training dataset, so this method can greatly reduce the size of the training dataset and improve the training speed. Finally, simulate with linear separation data and non-linear separation data. And the experimental results show the method is effective.
Keywords :
data analysis; support vector machines; Euclidean distance; SVM; distribution feature; linear separation data; nonlinear separation data; quasiclassification hypeplane; support vector machine; training dataset; training speed; Classification algorithms; Educational institutions; Equations; Euclidean distance; Kernel; Support vector machines; Training; Reduced Sample; Support Vector; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2012 3rd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4673-0914-1
Type :
conf
DOI :
10.1109/ICSSEM.2012.6340770
Filename :
6340770
Link To Document :
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