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Abstract
En 中文
Support vector machines with different kernel functions are useful for binary classification. In this paper, a novel support vector machine model with feature mapping is proposed. Feature mapping is achieved based on the equivalence of the recently proposed soft quadratic surface support vector machine. In this model, a technique for pre-selecting training points is initially utilized to improve robustness. Then, the feature mapping and kernel trick are combined to handle datasets with different distributions, particularly nonlinearly separable datasets. This model can be transformed into a convex quadratic programming problem, which is efficiently solved by applying the sequential minimal optimization algorithm. Finally, numerical tests on several artificial and benchmark datasets reveal the superior performance of the proposed method over other well-known classification methods. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Support vector machine
Kernel function
Quadratic surface
Classification
Feature mapping
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
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