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.
Highlights
Many methods of data mining exist, including machine learning which is a major research direction of artificial intelligence
When the upper bound of data errors is known, the method of data perturbation analysis is used to derive the upper bounds of the optimal solution and its corresponding optimal decision function
Support vector classifier plays an important role in machine learning and data mining
Summary
Many methods of data mining exist, including machine learning which is a major research direction of artificial intelligence. 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. The primary problem of the standard support vector classifier (SVC) is to
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