Study on Method of On-Line Identification for Complex Nonlinear Dynamic System Based on SVM, Proceedings of 2005 International Conference on, 2005. ,
Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme, Pattern Recognition with Support Vector Machines, pp.354-369, 2002. ,
DOI : 10.1007/3-540-45665-1_28
Neural Networks for Pattern Recognition, 1995. ,
Neurofuzzy Adaptive Modelling and Control, 1994. ,
On the modelling of nonlinear dynamic systems using support vector neural networks, Engineering Applications of Artificial Intelligence, vol.14, issue.2, pp.105-113, 2001. ,
DOI : 10.1016/S0952-1976(00)00069-5
LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001. ,
DOI : 10.1145/1961189.1961199
Choosing multiple parameters for support vector machines, Machine Learning, pp.131-159, 2002. ,
Neural networks for nonlinear dynamic system modelling and identification, International Journal of Control, vol.4, issue.2, pp.319-346, 1992. ,
DOI : 10.1142/S0129065790000102
Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, vol.2, issue.2, pp.302-309, 1991. ,
DOI : 10.1109/72.80341
URL : http://eprints.soton.ac.uk/251135/1/00080341.pdf
Identification of nonlinear systems using generalized kernel models, IEEE Transactions on Control Systems Technology, vol.13, issue.3, pp.401-411, 2005. ,
DOI : 10.1109/TCST.2004.841652
An Introduction to Support Vector Machines and other kernel-based learning methods, 2000. ,
DOI : 10.1017/CBO9780511801389
A Fast Parallel Optimization for Training Support Vector Machine, Lecture Notes in Computer Science, vol.2734, pp.96-105, 2003. ,
DOI : 10.1007/3-540-45065-3_9
Fast SVM training algorithm with decomposition on very large data sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.4, pp.603-618, 2005. ,
DOI : 10.1109/TPAMI.2005.77
Support vector machines for system identification. Control'98, UKACC International Conference on, 1998. ,
Regularization networks and support vector machines, Advances in Computational Mathematics, vol.13, issue.1, pp.1-50, 2000. ,
DOI : 10.1023/A:1018946025316
Working set selection using the second order information for training SVM, Journal of Machine Learning Research, vol.6, pp.1889-1918, 2005. ,
Evolutionary tuning of multiple SVM parameters, Proc. of the 12th Europ. Symp. on Artificial Neural Networks (ESANN), pp.519-524, 2004. ,
DOI : 10.1016/j.neucom.2004.11.022
Sparse kernel regression modelling based on l1 significant vector learning, Int. Conf. on Neural Networks and Brain (ICNN&B), 2005. ,
Exploiting generative models in discriminative classifiers, NIPS, pp.487-493, 1998. ,
Making large-scale support vector machine learning practical, Schölkopf et al. [36], pp.169-184 ,
Solving the quadratic programming problem arising in support vector classification, Schölkopf et al. [36], pp.147-167 ,
Improvements to Platt's SMO Algorithm for SVM Classifier Design, Neural Computation, vol.13, issue.3, pp.637-649, 2001. ,
DOI : 10.1080/10556789208805504
Ho???Kashyap with Early Stopping Versus Soft Margin SVM for Linear Classifiers ???An Application, Yin et al. [44], pp.524-530 ,
DOI : 10.1007/978-3-540-28647-9_87
URL : https://hal.archives-ouvertes.fr/hal-00120606
A trainable feature extractor for handwritten digit recognition, Pattern Recognition, vol.40, issue.6, 2006. ,
DOI : 10.1016/j.patcog.2006.10.011
URL : https://hal.archives-ouvertes.fr/hal-00018426
Support Vector Regression for the simultaneous learning of a multivariate function and its derivatives, Neurocomputing, vol.69, issue.1-3, pp.42-61, 2005. ,
DOI : 10.1016/j.neucom.2005.02.013
Time series prediction using support vector machines, the orthogonal and the regularized orthogonal least-squares algorithms, International Journal of Systems Science, vol.9, issue.10, pp.811-821, 2002. ,
DOI : 10.1080/002071797223631
Large scale kernel regression via linear programming, Machine Learning, pp.255-269, 2002. ,
Knowledge-based kernel approximation, J. Mach. Learn. Res, vol.5, pp.1127-1141, 2004. ,
DOI : 10.1109/tnn.2006.886354
Nonlinear Knowledge in Kernel Approximation, IEEE Transactions on Neural Networks, vol.18, issue.1, 2005. ,
DOI : 10.1109/TNN.2006.886354
Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations, Containing Papers of a Mathematical or Physical Character, pp.415-446, 1909. ,
DOI : 10.1098/rsta.1909.0016
Introduction to radial basis function networks, 1996. ,
Training support vector machines: an application to face detection, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.130-136, 1997. ,
DOI : 10.1109/CVPR.1997.609310
Fast training of support vector machines using sequential minimal optimization, Schölkopf et al. [36], pp.185-208 ,
Support vector machines for 3D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.6, pp.637-646, 1998. ,
DOI : 10.1109/34.683777
Robust Regression and Outlier Detection, 2003. ,
DOI : 10.1002/0471725382
Advances in kernel methods : Support vector learning, 1999. ,
New Support Vector Algorithms, Neural Computation, vol.20, issue.5, pp.1207-1245, 2000. ,
DOI : 10.1016/S0893-6080(98)00032-X
Nonlinear blackbox modeling in system identification : a unified overview, Automatica, issue.12, pp.311691-1724, 1995. ,
Linear programs for automatic accuracy control in regression, 9th International Conference on Artificial Neural Networks: ICANN '99, pp.575-580, 1999. ,
DOI : 10.1049/cp:19991171
A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004. ,
DOI : 10.1023/B:STCO.0000035301.49549.88
The nature of statistical learning theory, 1995. ,
Statistical learning theory, 1998. ,
Nonlinear dynamic system identification using least squares support vector machine regression, Proceedings of 2004 International Conference on, 2004. ,
Nonlinear System Identification Based on an Improved Support Vector Regression Estimator, pp.586-591 ,
DOI : 10.1007/978-3-540-28647-9_96