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Scholkopf, B., Burges, C.J.C. and Smola, A.J. (1999) Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA, 327-352.

has been cited by the following article:

• TITLE: Data Perturbation Analysis of the Support Vector Classifier Dual Model

  AUTHORS: Chun Cai, Xikui Wang

  KEYWORDS: Support Vector Classifier, Partial Derivative, Sensitivity, Stability

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.11 No.10, October 23, 2018

  ABSTRACT: 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.