scholarly journals Incremental Training of Support Vector Machines Using Truncated Hypercones

Author(s):  
Shinya Katagiri ◽  
Shigeo Abe
2011 ◽  
Vol 301-303 ◽  
pp. 677-681
Author(s):  
Liang Qin ◽  
Hong Wei Yin ◽  
Xian Jun Shi ◽  
Zhi Cai Xiao

In order to figure out the deficiency of the SVM on extensive sample, nature of SV is studied in this paper. An improved incremental training algorithm is put forward based on dimensional of samples. A chosen gene which got by density and distance criterion is used in this method. In this method the number of training samples is decreased and the space information is keeped. So, the training speed is improved while the precision is not reduced. And the simulation proved the efficiency of this method.


2005 ◽  
Vol 16 (1) ◽  
pp. 114-131 ◽  
Author(s):  
A. Shilton ◽  
M. Palaniswami ◽  
D. Ralph ◽  
A.C. Tsoi

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