| Abstrakt: |
A new second-order cone programming (SOCP) formulation inspired by the soft-margin linear programming support vector machine (LP-SVM) formulation and cost-sensitive framework is proposed. Our proposed method maximizes the slack variables related to each class by appropriately relaxing the bounds on the VC dimension using the l ∞ -norm, and penalizes them using the corresponding regularization parametrization to control the trade-off between margin and slack variables. The proposed method has two main advantages: firstly, a flexible classifier is constructed that extends the advantages of the soft-margin LP-SVM problem to the second-order cone; secondly, due to the elimination of a conic restriction, only two SOCP problems containing second-order cone constraints need to be solved. Thus similar results to the SOCP-SVM problem are obtained with less calculational effort. Numerical experiments show that our method achieves the better classification performance than the conventional SOCP-SVM formulations and standard linear SVM formulations. [ABSTRACT FROM AUTHOR] |
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