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http://dx.doi.org/10.5351/CKSS.2008.15.3.441

Multiclass Classification via Least Squares Support Vector Machine Regression  

Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu)
Bae, Jong-Sig (Department of Mathematics, Sungkyunkwan University)
Hwang, Chang-Ha (Division of Information and Computer Science, Dankook University)
Publication Information
Communications for Statistical Applications and Methods / v.15, no.3, 2008 , pp. 441-450 More about this Journal
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
Classification; cross validation; least squares support vector machine; multiclass; one-against-all; support vector machine;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Kimeldorf, G. S. and Wahba, G. (1971). Some results on Tchebyche$\pm$an spline functions, Journal of Mathematical Analysis and Applications, 33, 82-95   DOI
2 Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations, Philosophical Transactions of the Royal Society of London, Series A, 209, 415-446
3 Dietterich, T. G. and Bakiri, G. (1995). Solving multiclass learning problems via errorcorrecting output codes, Journal of Artificial Intelligence Research, 2, 263-286
4 Lee, Y., Lin, Y. andWahba, G. (2001). Multicategory support vector machines, Technical Report 1043, In Proceeding of the 33rd Symposium on the Interface
5 Vapnik, V. N. (1998). Statistical Learning Theory, John Wieley & Sons, New York
6 Suykens, J. A. K. and Vandewalle, J. (1999a). Least square support vector machine classifiers, Neural Processing Letters, 9, 293-300   DOI
7 Suykens, J. A. K. (2001). Nonlinear modelling and support vector machines, In Proceeding of the IEEE Instrumentation and Measurement Technology Conference, 287-294
8 Vapnik, V. N. (1995). The Nature of Statistical Learning Theory, Springer, New York
9 Rifkin, R. and Klautau, A. (2004). In defense of one-vs-all classification, The Journal of Machine Learning Research, 5, 101-141
10 Weston, J. and Watkins, C. (1998). Multi-Class SVM, Technical Report, 98-104, Royal Holloway University of London
11 Allwein, E. L., Schapire, R. E. and Singer, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers, Journal of Machine Learning Research, 1,113-141   DOI
12 Suykens, J. A. K. and Vandewalle, J. (1999b). Multiclass least squares support vector machines, In Proceeding of the International Joint Conference on Neural Networks, 900-903
13 Shim, J., Hong, D. H., Kim, D. H. and Hwang, C. (2007). Multinomial kernel logistic regression via bound optimization approach, The Korean Communications in Statistics, 14, 507-516   과학기술학회마을   DOI   ScienceOn