JOURNAL ARTICLE

Multiclass Classification via Least Squares Support Vector Machine Regression

Jooyong ShimJong-Sig BaeChangha Hwang

Year: 2008 Journal:   Communications for Statistical Applications and Methods Vol: 15 (3)Pages: 441-450   Publisher: Korean Statistical Society

Abstract

In this paper we propose a new method for solving multiclass problem with least squares support vector machine(LS-SVM) regression. This method implements one-against-all scheme which is as accurate as any other approach. We also propose cross validation(CV) method to select effectively the optimal values of hyper-parameters which affect the performance of the proposed multiclass method. Experimental results are then presented which indicate the performance of the proposed multiclass method.

Keywords:
Support vector machine Multiclass classification Least squares support vector machine Structured support vector machine Regression Least-squares function approximation Artificial intelligence Computer science Relevance vector machine Mathematics Cross-validation Machine learning Pattern recognition (psychology) Data mining Statistics

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3
Cited By
0.00
FWCI (Field Weighted Citation Impact)
4
Refs
0.03
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Citation History

Topics

Face and Expression Recognition
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Blind Source Separation Techniques
Physical Sciences →  Computer Science →  Signal Processing
Advanced Algorithms and Applications
Physical Sciences →  Engineering →  Control and Systems Engineering

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