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Emphatic Constraints Support Vector Machines for Multi-class Classification
Support vector machine (SVM) formulation has been originally developed for binary classification problems. Finding the direct formulation for multi-class case is not easy but still an on-going research issue. This paper presents a novel approach for multi-class SVM by modifying the training phase of...
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Main Authors: | , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | Support vector machine (SVM) formulation has been originally developed for binary classification problems. Finding the direct formulation for multi-class case is not easy but still an on-going research issue. This paper presents a novel approach for multi-class SVM by modifying the training phase of the SVM. First, we propose the Emphatic Constraints Support Vector Machines (ECSVM) as a new powerful classification method. Then, we extend our method to find efficient multi-class classifiers. We evaluate the performance of the proposed scheme by means of real world data sets. The obtained results show the superiority of our method. |
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DOI: | 10.1109/EMS.2009.61 |