A structural information-based twin-hypersphere support vector machine classifier
Twin-hypersphere support vector machine (THSVM) for binary pattern recognition aims at generating two hyperspheres in the feature space such that each hypersphere contains as many as possible samples in one class and is as far as possible from the other one. THSVM has a fast learning speed since it...
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| Veröffentlicht in: | International journal of machine learning and cybernetics 2017-02, Vol.8 (1), p.295-308 |
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| container_title | International journal of machine learning and cybernetics |
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| creator | Peng, Xinjun Kong, Lingyan Chen, Dongjing |
| description | Twin-hypersphere support vector machine (THSVM) for binary pattern recognition aims at generating two hyperspheres in the feature space such that each hypersphere contains as many as possible samples in one class and is as far as possible from the other one. THSVM has a fast learning speed since it solves two small sized support vector machine (SVM)-type quadratic programming problems (QPPs). However, it only simply considers the prior class-based structural information in the optimization problems. In this paper, a structural information-based THSVM (STHSVM) classifier for binary classification is presented. This proposed STHSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. In addition, it also leads to a fast learning speed since this STHSVM solves a series of smaller-sized QPPs compared with THSVM. Experimental results demonstrate that STHSVM is superior in generalization performance to other classifiers. |
| doi_str_mv | 10.1007/s13042-014-0323-4 |
| format | Article |
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J. Mach. Learn. & Cyber</addtitle><description>Twin-hypersphere support vector machine (THSVM) for binary pattern recognition aims at generating two hyperspheres in the feature space such that each hypersphere contains as many as possible samples in one class and is as far as possible from the other one. THSVM has a fast learning speed since it solves two small sized support vector machine (SVM)-type quadratic programming problems (QPPs). However, it only simply considers the prior class-based structural information in the optimization problems. In this paper, a structural information-based THSVM (STHSVM) classifier for binary classification is presented. This proposed STHSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. In addition, it also leads to a fast learning speed since this STHSVM solves a series of smaller-sized QPPs compared with THSVM. 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| language | eng |
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| source | Springer Nature - Complete Springer Journals; ProQuest Central |
| subjects | Algorithms Artificial Intelligence Classifiers Complex Systems Computational Intelligence Control Engineering Hyperspheres Learning Mechatronics Optimization Original Article Pattern Recognition Quadratic programming Robotics Support vector machines Systems Biology |
| title | A structural information-based twin-hypersphere support vector machine classifier |
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