An Improved Incremental Training Algorithm of Support Vector Machines

Abstract:

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.

• Periodical: Advanced Materials Research (Volumes 301-303)
• Pages: 677-681
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.301-303.677
• Online since: July 2011
• Authors: Liang Qin, Hong Wei Yin, Xian Jun Shi, Zhi Cai Xiao
• Keywords: Incremental Training, Support Vector, Support Vector Machine (SVM)

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