Key Problem in Support Vector Machine Model
This study found the development direction of SVM, the research content is the most crucial and fundamental nature in SVM, if achieve this paper targets, it will promote the further application of SVM, and have important theoretical value; In addition, this study are The basic work of nuclear analytical methods, the results can be directly applied to the field of recognition pattern based on nuclear analytical methods (such as Kernel Principal Component Analysis and Kernel Fisher method), so the research results of this paper has good generalized values.
Shengyi Li, Yingchun Liu, Rongbo Zhu, Hongguang Li, Wensi Ding
X. L. Qu et al., "Key Problem in Support Vector Machine Model", Applied Mechanics and Materials, Vols. 34-35, pp. 1351-1354, 2010