(Zhang H R, Wang X D. Incremental and online learning algorithm for regression least squares support vector machine[J]. Chinese J of Computers, 2006, 29(3): 400-406.)
(Zhang S N,Wang F L, You F Q, et al. Robust least squares support vector machine based on robust learning algorithm and its application[J]. Control and Decision, 2010, 25(8): 1169-1177.)
(Zhang S N, Wang F L, He D K, et al. Modeling method of online robust least-squares-support-vector regression[J]. Control Theory & Applications, 2011, 28(11): 1601-1606.)
[7]
Jiao L, Bo L, Wang L. Fast sparse approximation for least squares support vector machine[J]. IEEE Trans on Neural Networks, 2007, 18(3): 685-697.
[8]
Cortez P, Cerdara A, Tsanas A. Uc irvine machine learning repository[DB/OL]. [2009-10-07](2012-08-20). http://archive.ics.uci.edulmll.
[9]
Vapnik V N. The nature of statistical learning theory[M]. New York: Springer-Verlag Press, 1995: 138-170.
[10]
Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines[C]. Proc of IEEE Signal Processing Society Workshop. New York: IEEE Press, 1997: 276-285.
[11]
Joachims T. Making large-scale SVM learning practical[C]. Advances in Kernel Methods: Support Vector Machine. Cambridge: MIT Press, 1999: 169-184.
[12]
Collobert R, Bengio S. SVMTorch: Support vector machines for large-scale regression problems[J]. J of Machine Learning Research, 2001, 1(2): 143-160.
[13]
Shevade S K, Keerthi S S, Bhattacharyya C, et al. Improvements to the SMO algorithm for SVM regression[J]. IEEE Trans on Neural Networks, 2000, 11(5): 1188-1193.
[14]
Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letter, 1999, 9(3): 293-300.
[15]
Suykens J A K, Brabanter J D, Lukas L, et al. Weighted least squares support vector machines: Robustness and sparse approximation[J]. Neurocomputing, 2002, 48(1): 85-105.
[16]
Shim J, Hwang C, Nau S. Robust LSSVM regression using fuzzy ??-means clustering[C]. Proc of the 2nd Int Conf on Natural Computation. Xi’an: Springer Press, 2006, 9: 157-166.
[17]
Suykens J A K, Lukas L, Vandewalle J. Sparse approximation using least squares vector machines[C]. Proc of IEEE Int Symposium on Circuits and Systems. New Jersey: IEEE Press, 2000: 757-760.
[18]
Zhao Y P, Sun J G. Recursive reduced least squares support vector regression[J]. Pattern Recognition, 2009, 42(5): 837-842.
[19]
Zhao Y P, Sun J G, Zhong H D, et al. An improved recursive reduced least squares support vector regression[J]. Neurocomputing, 2012, 87(3): 1-9.
[20]
Cauwenberghs G, Poggio T. Incremental and decremental support vector machine learning[C]. Proc of the 14th Annual Neural Information Processing Systems Conf. Colorado, 2001: 409-423.
[21]
Wang H, Pi D Y, Sun Y X. Online SVM regression algorithm-based adaptive inverse control[J]. Neurocomputing, 2007, 70(4): 952-959.
