From maxi-min margin machine classification to regression
The maxi-min margin machine (M 4 ) algorithm, contrast to the traditional support vector machine (SVM) algorithm, gives a more robust solution and gets better generalization performance. In this paper we extend the M 4 classification algorithm to deal with regression problem, and propose a novel reg...
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| creator | Xiaoming Wang Xiangnian Huang |
| description | The maxi-min margin machine (M 4 ) algorithm, contrast to the traditional support vector machine (SVM) algorithm, gives a more robust solution and gets better generalization performance. In this paper we extend the M 4 classification algorithm to deal with regression problem, and propose a novel regression method. This method inherits the characteristics of M 4 such as good robustness and generalization performance. In this paper we discuss the linear and nonlinear case of the proposed method, and experimental results indicate its effectiveness and better robustness and generalization performance compared with the traditional SVR algorithm. |
| doi_str_mv | 10.1109/MEC.2011.6025952 |
| format | Conference Proceeding |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Classification algorithms Educational institutions Kernel kernel method maxi-min margin machine Optimization Robustness Support vector machines support vector regression Training |
| title | From maxi-min margin machine classification to regression |
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