원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.8 No.5
2015.10
pp.65-76
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSSVM, Multi-GEPSVM, Multi-TWSVM and our Multi-LSTSVM on both UCI datasets and ORL, YALE face datasets illustrate the effectiveness of the proposed method.
목차
Abstract
1. Introduction
2. Background
2.1. Least Squares Support Vector Machine (LSSVM)
2.2. Least Squares Twin Support Vector Machine (LSTSVM)
3. Multi-LSTSVM
3.1. Linear Multi-LSTSVM
3.2. Nonlinear Multi-LSTSVM
4. Experimental Results
4.1. UCI Datasets
4.2. Image Classification
5. Conclusions
Acknowledgments
References
1. Introduction
2. Background
2.1. Least Squares Support Vector Machine (LSSVM)
2.2. Least Squares Twin Support Vector Machine (LSTSVM)
3. Multi-LSTSVM
3.1. Linear Multi-LSTSVM
3.2. Nonlinear Multi-LSTSVM
4. Experimental Results
4.1. UCI Datasets
4.2. Image Classification
5. Conclusions
Acknowledgments
References
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자료제공 : 네이버학술정보