Study on least squares support vector machines algorithm and its application

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 2005, Issue , 2005, Pages 686-688

  Zhang, Ming Guang ^a   Li, Zhan Ming ^a   Li, Wen Hui ^a  

^a LANZHOU UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Identification,soft sensor modeling; Least Squares Support Vector Machines (LS SVM); SVM

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONTROL NONLINEARITIES; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; NONLINEAR CONTROL SYSTEMS; PATTERN RECOGNITION; VECTORS;

INEQUALITY CONSTRAINTS; SOFT-SENSOR MODELING; SUPPORT VECTOR MACHINES;

DECISION SUPPORT SYSTEMS;

EID: 33845910086     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2005.116     Document Type: Conference Paper

References (8)

1. V. Vapnik, The Nature of statistical Learning Theory, New York: Springer-Verlag, 1995.
2. V. Vapnik, The Nature, of statistical Learning Theory, New York: Springer-Verlag, 1999.
3. B. SchVolkopf, K.-K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio, V. Vapnik, Comparing support vector machines with Gaussian kernels to radial basis function classifiers, IEEE Trans. Signal Process. 45 (11) (1997) 2758-2765.
4. B. SchVolkopf, C. Burges, A. Smola (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press. Cambridge, MA, 1998.
5. A. Smola, B. SchVolkopf, On a kernel-based method for pattern recognition, regression, approximation and operator inversion, Algorithmica, 22 (1998) 211-231.
6. J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett. 9 (3) (1999) 293-300.
7. J.A.K. Suykens, J. Vandewalle, Optimal Control by Least Squares Support Vector Machines, Neural Networks 14 (1) (2001) 23-35.
8. Ming-guang Zhang,Wei-wuYan,Zhan-ting Yuan. Study on Nonlinear System Identification Based on Support Vector Machines. IEEE ICMLC2004,Shanghai, China, Aug.2004.pp3287-3290.