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基于线性无关度的稀疏最小二乘支持向量回归机

DOI: 10.11834/jig.20090620

Keywords: 最小二乘支持向量回归机,线性无关,近似基,不完全抛弃

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Abstract:

基于线性无关度提出了一种在高维特征空间中选择近似基的方法,并采用不完全抛弃法,充分利用非支持向量中的信息来建立稀疏最小二乘支持向量回归机的数学模型。另外,采用递推法加快了其模型的建立。该模型在保持预测精度基本不变的情况下,使支持向量的数目大大减少。最后,通过3个UCI数据集验证了该模型的有效性。

References

[1]  Vapnik V N. The Nature of Statistical Learning Theory[M]. New York : Springer-Verlag, 1995, 85-124.
[2]  IDbeyli E D. Muhiclass support vector machines for diagnosis of erythemato-squamous diseases[J]. Expert Systems with Applications, 2008, 35(4) : 1733-1740.
[3]  Wang W J, Men C Q, Lu W Z. Online prediction model based on support vector machine [J] . Neurocomputing, 2008, 71 (4- 6 ) : 550-558.
[4]  Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3) : 293-300.
[5]  Suykens J A K, Van Gestel T, De Brabanter J, et al. Least Squares Support Vector Machines[M]. Singapore: World Scientific, 2002.
[6]  Suykens J A K, Lukas L, Dooren P V, et al. Least squares support vector machine classifiers: a large scale algorithm [A] . In:Proceedings of the European Conference on Circuit Theory and Design [C] , Stresa, Italy, 1999 : 839-842.
[7]  Chu W, Ong C J, Keerthi S S. An improved conjugate gradient scheme to the solution of least squares SVM [J]. IEEE Transactions on Neural Networks, 2005, 16(2): 498-501.
[8]  Keerthi S S, Shevade S K. SMO algorithm for least squares SVM [ A ]. In : Proceedings of the International joint Conference on Neural Networks 2003[C], Portland, OR, USA, 2003: 2088-2093.
[9]  Chua K S. Efficient computations for large least squares support vector machine classifiers [J]. Pattern Recognition Letters, 2003, 24(1-3) : 75-80.
[10]  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(1-4): 85-105.
[11]  Gao J B, Shi D, Liu X M. Significant vector learning to construct sparse kernel regression models [ J ]. Neural Networks, 2007 (7) : 791-798.
[12]  Suykens J A K, Lukas L, Vandewalle J. Sparse approximation using least squares support vector machines[ A]. In: Proceeding of IEEE International Symposium on Circuits and System [C], Geneva, Switz, 2000 : 757-760.
[13]  Kruif B J, Vries J A. Pruning error minimization in least squares support vector machines[J]. IEEE Transactions on Neural Networks, 2003, 14(3) : 696-702.
[14]  Kuh A, Wilde P D. Comments on "pruning error minimization in least squares support vector machines" [J]. IEEE Transactions on Neural Networks, 2007, 18(2): 606-609.
[15]  Hoegaerts L, Suykens J A K, Vandewalle J, et al. A comparison of pruning algorithms for sparse least squares support vector machines [ A ] . In: Proceeding of International Conference on Neural Information Processing 2004 [C], Calcutta, India, 2004: 1247-1253.
[16]  Zeng X Y, Chen X W. SMO-based pruning methods for sparse least squares support vector machines[J]. IEEE Transactions on Neural Networks,2005, 16(6) : 1541-1546.
[17]  Jiao L C, Bo L F, Wang L. Fast sparse approximation for least squares support vector machine [J]. IEEE Transactions on Neural Networks, 2007, 15(3 ): 685-697.
[18]  Espinoza M, Suykens J A K, Moor B D. Least squares support vector machines and primal space estimation [ A ]. In: Proceeding of the 42nd IEEE Conference on Decision and Control [C], Maui, HI, USA, 2003: 3451-3456.
[19]  陈爱军 宋执环 李平.基于矢量基学习的最小二乘支持向量机建模[J].控制理论与应用,2007,(1):.
[20]  Gan L Z, Liu H K, Sun Y X. Sparse least squares support vector machine for function estimation [ A ] . In: Proceedings of the 3rd International Symposium on Neural Networks[C], Chengdu, China, 2006 : 1016-1021.
[21]  更多...
[22]  张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004.
[23]  Murphu P M, Aha D W. UCI Repository of Machine Learning Database [EB/OL]. 2007, available from : http ://www. ics. uci. edu/- mlearn/MLRepository. html.
[24]  Zhao Y, Keong K C. Fast leave-one-out evaluation and improvement on inference for LS-SVMs [ A ] . In: Proceedings of the 17th International Conference on Pattern Recognition [C], Cambridge, UK, 2004: 494-497.
[25]  An S J, Liu W Q, Venkatesh S. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression[J]. Pattern Recognition, 2007, 40(8) : 2154-2162.

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