Title : ( Emphatic Constraints Support Vector Machines for Multi-class Classification )
Authors: Mostafa Sabzekar , Mahmoud Naghibzadeh , Hadi Sadoghi Yazdi , Sohrab Effati ,
Full TextAbstract
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.
Keywords
, Support vector machines; multi, class classification; fuzzy inequality; emphatic constraints;
@inproceedings{paperid:1015415,
author = {Sabzekar, Mostafa and Naghibzadeh, Mahmoud and Sadoghi Yazdi, Hadi and Effati, Sohrab},
title = {Emphatic Constraints Support Vector Machines for Multi-class Classification},
booktitle = {Emphatic Constraints Support Vector Machines for Multi-class Classification},
year = {2009},
keywords = {Support vector machines; multi-class
classification; fuzzy inequality; emphatic constraints;},
}
%0 Conference Proceedings
%T Emphatic Constraints Support Vector Machines for Multi-class Classification
%A Sabzekar, Mostafa
%A Naghibzadeh, Mahmoud
%A Sadoghi Yazdi, Hadi
%A Effati, Sohrab
%J Emphatic Constraints Support Vector Machines for Multi-class Classification
%D 2009
