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A Review on Support Vector Machines for Classification Problems


Affiliations
1 Department of Electronics & Computer Engineering, NIT Arunachal Pradesh, Yupia, India
2 Faculty of Basic & Applied Science, NIT Arunachal Pradesh, Yupia, India
     

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Support Vector Machine (SVM) is one of the best techniques to classify the data into multiple classes. In the recent years, support vector machine has been used extensively for classification problems. The main advantage of using support vector machines is its better generalization ability while using higher dimension of data. This paper gives a review on the formulation of some important variants of SVM i.e. hard margin SVM, soft margin SVM, Least Squares Support Vector Machine (LSSVM), Twin Support Vector Machine (TWSVM) and Least Squares Support Vector Machine (LS-TWSVM). To check the effectiveness of these methods, numerical experiments are performed on artificial and real world datasets.

Keywords

Support Vector Machine (SVM), Twin Support Vector Machine (TWSVM), Least Squares method, Artificial Neural Network (ANN).
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  • A Review on Support Vector Machines for Classification Problems

Abstract Views: 336  |  PDF Views: 5

Authors

Bharat Richhariya
Department of Electronics & Computer Engineering, NIT Arunachal Pradesh, Yupia, India
Deepak Gupta
Department of Electronics & Computer Engineering, NIT Arunachal Pradesh, Yupia, India
Shakti Prasad
Faculty of Basic & Applied Science, NIT Arunachal Pradesh, Yupia, India
Kamalini Acharjee
Department of Electronics & Computer Engineering, NIT Arunachal Pradesh, Yupia, India

Abstract


Support Vector Machine (SVM) is one of the best techniques to classify the data into multiple classes. In the recent years, support vector machine has been used extensively for classification problems. The main advantage of using support vector machines is its better generalization ability while using higher dimension of data. This paper gives a review on the formulation of some important variants of SVM i.e. hard margin SVM, soft margin SVM, Least Squares Support Vector Machine (LSSVM), Twin Support Vector Machine (TWSVM) and Least Squares Support Vector Machine (LS-TWSVM). To check the effectiveness of these methods, numerical experiments are performed on artificial and real world datasets.

Keywords


Support Vector Machine (SVM), Twin Support Vector Machine (TWSVM), Least Squares method, Artificial Neural Network (ANN).

References