Online least squares support vector machines based on wavelet and its applications

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4493 LNCS, Issue PART 3, 2007, Pages 416-425

  Zhang, Qian ^a   Fan, Fuling ^a   Wang, Lan ^a  

^a ZHONGYUAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; DATA STRUCTURES; LEARNING ALGORITHMS; NONLINEAR SYSTEMS; ONLINE SYSTEMS; WAVELET TRANSFORMS;

WAVELET FRAMES; WAVELET KERNELS;

SUPPORT VECTOR MACHINES;

EID: 38049134215     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72395-0_53     Document Type: Conference Paper

References (20)

1. Vapnik, V.: The Nature of Statistical Learning Theory. New York: Springer-Verlag (1995)
2. Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2 (1998) 955-974
3. Drezet, P.M.L., Harrison, R.F.: A New Method for Sparsity Control in Support Vector Classi"cation and Regression. Pattern Recognition 34 (2001) 111-125
4. Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9 (1999) 293-300
5. Chua, K.S.: Efficient Computations for Large Least Square Support Vector Machine Classifiers. Pattern Recognition Letters 24 (2003) 75-80
6. Tsujinishi, D., Abe, S.: Fuzzy Least Squares Support Vector Machines for Multiclass Problems. Neural Networks 16 (2003) 785-792
7. Saunders, C., Gammerman, A., Vovk, V.: Ridge Regression Learning Algorithm in Dual Variables. Proceedings of the 15th International Conference on Machine Learning, Morgan Kaufmann (1998) 515-521
8. Gestel, T.V., Suykens, J.A.K., Baestaens, D. et, al: Financial Time Series Prediction Ssing Least Squares Support Vector Machines within the Evidence Framework. IEEE Trans. on Neural Networks 12 (2001) 809-821
9. Suykens, J.A.K., Brabanter, J.D., Lukas, L., Vandewalle, J.: Weighted Least Squares Support Vector Machines Robustness and Sparse Approximation. Neurocomputing 48 (2002) 85-105
10. Kruif, B.J.d., Vries, T.J.A.D.: Pruning Error Minimization in Least Squares Support Vector Machines. IEEE Trans. on Neural Networks 14 (2003) 696-702
11. Mercer, J.: Functions of Positive and Negative Type and Their Connection with the Theory of Integral Equation. Transactions of the London Philosophical Society. A-209 (1909) 415-446
12. Smola, A., Schö lkopf, B., Müller, K.R.: The Connection between Regularization Operators and Support Vector Kernels. Neural Networks 11 (1998) 637-649
13. Burges, C.J.C.: Geometry and Invariance in Kernel Based Methods. Advance in Kernel Methods-Support Vector Learning. Cambridge, MA: MIT Press. London (1999) 89-116
14. Zhang, Q.: Using Wavelet Network in Nonparametric Estimation. IEEE Trans. on Neural Networks 8 (1997) 227-236
15. Wu, F., Zhao, Y.: Least Squares Littlewood-Paley Wavelet Support Vector Machine. Lecture Notes in Computer Science 3789 (2005) 462-472
16. Zhang, L., Zhou, W.D., Jiao, L.C.: Wavelet Support Vector Machine. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics 34 (2004) 34-37
17. Kuh, A.: Adaptive Least Square Kernel Algorithms and Applications. Proceedings of International Joint Conference on Neural Networks (2002) 2104-2107
18. Kuh, A.: Adaptive Kernel Methods for CDMA Systems. Proceedings of International Joint Conference on Neural Networks (2001) 1404-1409
19. Ralaivola, L., d'Alche-Buc, F.: Incremental Support Vector Machine Learning: a Local Approach. Lecture Notes in Computer Science 2130 Springer-Verlag, Berlin Heidelberg New York (2001) 322-330
20. Narendra, K., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. on Neural Networks 1 (1990) 4-27