文献类型：期刊文献

中文题名：Data Perturbation Analysis of the Support Vector Classifier Dual Model

英文题名：Data Perturbation Analysis of the Support Vector Classifier Dual Model

作者：Chun Cai[1];Xikui Wang[2]

第一作者：蔡春

机构：[1]College of Arts and Science, Beijing Union University, Beijing, China;[2]Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada

第一机构：北京联合大学应用文理学院

年份：2018

卷号：11

期号：10

起止页码：459-466

中文期刊名：软件工程与应用（英文）

语种：英文

中文关键词：Support;Vector;Classifier;Partial;Derivative;Sensitivity;Stability

外文关键词：Support Vector Classifier;Partial Derivative;Sensitivity;Stability

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.
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.