Support vector machine classification with noisy data: a second order cone programming approach

2010 ◽  
Vol 39 (7) ◽  
pp. 757-781 ◽  
Author(s):  
Theodore B. Trafalis ◽  
Samir A. Alwazzi
Keyword(s):  
Support Vector Machine ◽  
Noisy Data ◽  
Second Order ◽  
Programming Approach ◽  
Support Vector ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Support Vector Machine Classification ◽  
Second Order Cone

A novel second-order cone programming support vector machine model for binary data classification

2020 ◽  
Vol 39 (3) ◽  
pp. 4505-4513
Author(s):  
Guishan Dong ◽  
Xuewen Mu
Keyword(s):  
Support Vector Machine ◽  
Second Order ◽  
Misclassification Rate ◽  
Support Vector ◽  
Programming Formulation ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Second Order Cone ◽  
Soft Margin ◽  
Programming Support

The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l2-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters α and β are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.

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