Support Vector Machine Classification Methods: A Review and Comparison with Different Classifiers
Support Vector Machines (SVMs) have been extensively researched in the data mining and machinelearning 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.
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