期刊名称：Journal of Software Engineering and Applications
印刷版ISSN：1945-3116
电子版ISSN：1945-3124
出版年度：2018
卷号：11
期号：10
页码：459-466
DOI：10.4236/jsea.2018.1110027
语种：English
出版社：Scientific Research Publishing
摘要：The paper establishes a theorem of data perturbation analysis for the support vector classifier dual problem, from which the data perturbation analysis of the corresponding primary problem may be performed through standard results. This theorem derives the partial derivatives of the optimal solution and its corresponding optimal decision function with respect to data parameters, and provides the basis of quantitative analysis of the influence of data errors on the optimal solution and its corresponding optimal decision function. The theorem provides the foundation for analyzing the stability and sensitivity of the support vector classifier.
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