Data Mining and Knowledge Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Support Vector Machine Classification Methods:A Review and Comparison with Different Classifiers


Affiliations
1 Department of Computer Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
2 Charotar University of Science Technology (CHARUSAT), Education Campus, Changa, Gujarat, India
     

   Subscribe/Renew Journal


Support Vector Machines (SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification and regression tasks. Two special properties of SVMs are that they achieve (1) high generalization by maximizing the margin and (2) support an efficient learning of nonlinear functions by kernel trick. Many algorithms and their improvements have been proposed to train SVMs. This paper presents a comprehensive description of various SVM methods and compares SVM classifier with other classification methods.


Keywords

Classifiers, Machine Learning, Predictive Accuracy, Support Vector Machine (SVM).
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 188

PDF Views: 2




  • Support Vector Machine Classification Methods:A Review and Comparison with Different Classifiers

Abstract Views: 188  |  PDF Views: 2

Authors

Ankit P. Vaishnav
Department of Computer Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India
Amit P. Ganatra
Charotar University of Science Technology (CHARUSAT), Education Campus, Changa, Gujarat, India
C. K. Bhensdadia
Department of Computer Engineering, Dharmsinh Desai University, Nadiad, Gujarat, India

Abstract


Support Vector Machines (SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification and regression tasks. Two special properties of SVMs are that they achieve (1) high generalization by maximizing the margin and (2) support an efficient learning of nonlinear functions by kernel trick. Many algorithms and their improvements have been proposed to train SVMs. This paper presents a comprehensive description of various SVM methods and compares SVM classifier with other classification methods.


Keywords


Classifiers, Machine Learning, Predictive Accuracy, Support Vector Machine (SVM).