-
3
-
-
0032638628
-
Least squares support vector machine classifiers
-
SUYÖ KENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.
-
(1999)
Neural Processing Letters
, vol.9
, Issue.3
, pp. 293-300
-
-
Kens, J.A.K.S.1
Vandewalle, J.2
-
4
-
-
0036825528
-
Weighted least squares support vector machines: Robustness and spare approximation
-
SUYKENS J A K, BRANBANTER J K. Weighted least squares support vector machines: robustness and spare approximation[J]. Neurocomputing, 2002, 48(1): 85-105.
-
(2002)
Neurocomputing
, vol.48
, Issue.1
, pp. 85-105
-
-
Suykens, J.A.K.1
Branbanter, J.K.2
-
6
-
-
0344036310
-
Interpolation based kernel function's construction
-
Wu Tao, He Hangen, He Mingke. Interpolation based kernel function's construction[J]. Chinese Journal of Computers, 2003, 26(8): 990-996.
-
(2003)
Chinese Journal of Computers
, vol.26
, Issue.8
, pp. 990-996
-
-
Wu, T.1
He, H.2
He, M.3
-
7
-
-
0242288807
-
Model selection for support vector machine classification
-
GOLD C, SOLLICH P. Model selection for support vector machine classification[J]. Neurocomputing, 2003, 55(1-2): 221-249.
-
(2003)
Neurocomputing
, vol.55
, Issue.1-2
, pp. 221-249
-
-
Gold, C.1
Sollich, P.2
-
10
-
-
0036086881
-
Improved SVM regression using mixtures of kernels
-
Honolulu: Institute of Electrical and Electronics Engineers Inc.
-
SMITS G F, JORDAN E M. Improved SVM regression using mixtures of kernels[C]//Proceedings of the International Joint Conference on Neural Networks. Honolulu: Institute of Electrical and Electronics Engineers Inc., 2002, 3: 2785-2790.
-
(2002)
Proceedings of the International Joint Conference on Neural Networks
, vol.3
, pp. 2785-2790
-
-
Smits, G.F.1
Jordan, E.M.2
-
11
-
-
28444494043
-
An empirical assessment on the robustness of support vector regression with different kernels
-
Guangzhou: Institute of Electrical and Electronics Engineers Computer Society
-
LIU Jingxu, LI Jin, TAN Yuejin. An empirical assessment on the robustness of support vector regression with different kernels[C]//International Conference on Machine Learning and Cybernetics. Guangzhou: Institute of Electrical and Electronics Engineers Computer Society, 2005: 4289-4294.
-
(2005)
International Conference on Machine Learning and Cybernetics
, pp. 4289-4294
-
-
Liu, J.1
Li, J.2
Tan, Y.3
-
12
-
-
19044395306
-
Modeling of LS-SVM based on mixtures of kernels for MISO systems
-
ZHU Yanfei, WU Jianping, LI Qi, et al. Modeling of LS-SVM based on mixtures of kernels for MISO systems[J]. Control and Decision, 2005, 20(4): 417-425.
-
(2005)
Control and Decision
, vol.20
, Issue.4
, pp. 417-425
-
-
Zhu, Y.1
Wu, J.2
Li, Q.3
-
13
-
-
84887252594
-
Support vector method for function approximation, regression estimation, and signal processing
-
VAPNIK V, GOLOWICH S, SMOLA A. Support vector method for function approximation, regression estimation, and signal processing[J]. Neural Information Processing Systems, 1997, 9: 281-287.
-
(1997)
Neural Information Processing Systems
, vol.9
, pp. 281-287
-
-
Vapnik, V.1
Golowich, S.2
Smola, A.3
-
15
-
-
44049093391
-
The connection between regularization operators and support vector kernels
-
SMOLA A J, SCHÖLKOPF B. The connection between regularization operators and support vector kernels[J]. Neural Networks, 1998, 10: 1445-1454.
-
(1998)
Neural Networks
, vol.10
, pp. 1445-1454
-
-
Smola, A.J.1
Schölkopf, B.2
-
16
-
-
0038259114
-
Classes of kernels for machine learning: A statistics perspective
-
GENTON M G. Classes of kernels for machine learning: a statistics perspective[J]. Journal of Machine Learning Research, 2001, 2: 299-312.
-
(2001)
Journal of Machine Learning Research
, vol.2
, pp. 299-312
-
-
Genton, M.G.1
-
17
-
-
0742290039
-
Wavelet support vector machine
-
ZHANG Li, ZHOU Weida, JIAO Licheng. Wavelet support vector machine[J]. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, 2004, 34(1): 34-39.
-
(2004)
IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics
, vol.34
, Issue.1
, pp. 34-39
-
-
Zhang, L.1
Zhou, W.2
Jiao, L.3
-
18
-
-
33748697267
-
Support vector machine with scaling kernel and its application in dynamic system identification
-
Hu Dan, XIAO Jian, CHE Chang. Support vector machine with scaling kernel and its application in dynamic system identification[J]. Journal of Southwest Jiaotong University, 2006 4(41): 460-465.
-
(2006)
Journal of Southwest Jiaotong University
, vol.4
, Issue.41
, pp. 460-465
-
-
Hu, D.1
Xiao, J.2
Che, C.3
-
19
-
-
8844278523
-
Learning the kernel matrix with semi-definite programming
-
LANCKRIET G R, CRISTIANINI N, BARTLETT P. Learning the kernel matrix with semi-definite programming[J]. Journal of Machine Learning Research, 2004, 5(1): 27-72.
-
(2004)
Journal of Machine Learning Research
, vol.5
, Issue.1
, pp. 27-72
-
-
Lanckriet, G.R.1
Cristianini, N.2
Bartlett, P.3
-
20
-
-
34249042729
-
Multiple kernel learning for support vector regression
-
QIU S B, LANE T. Multiple kernel learning for support vector regression[DB/OL]. (2005-12-10)[2007-12-10]. http://www.cs.unm.edu/-treport/tr/05-12/QiuLane.
-
(2005)
-
-
Qiu, S.B.1
Lane, T.2
-
21
-
-
0346881149
-
Experimentally optimal v in support vector regression for different noise models and parameters settings
-
CHALIMOURDA A, SCHÖLKOPF B, SMOLA A J. Experimentally optimal v in support vector regression for different noise models and parameters settings[J]. Neural Networks, 2004, 17(1): 127-141.
-
(2004)
Neural Networks
, vol.17
, Issue.1
, pp. 127-141
-
-
Chalimourda, A.1
Schölkopf, B.2
Smola, A.J.3
-
22
-
-
20444505293
-
Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization
-
ÜSTÜN B, MELSSEN W J, OUDENHUIJZEN M. Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization[J]. Analytica Chimica Acta, 2005, 544(1-2): 292-305.
-
(2005)
Analytica Chimica Acta
, vol.544
, Issue.1-2
, pp. 292-305
-
-
Üstün, B.1
Melssen, W.J.2
Oudenhuijzen, M.3
-
23
-
-
0141869869
-
A pattern search for model selection of support vector regression
-
Philadephia: SIAM
-
MOMMA M, BENNETT B P. A pattern search for model selection of support vector regression[C]//Proceedings of SIAM Conference on Data Mining. Philadephia: SIAM, 2002: 261-274.
-
(2002)
Proceedings of SIAM Conference on Data Mining
, pp. 261-274
-
-
Momma, M.1
Bennett, B.P.2
-
24
-
-
0141794580
-
Optimizing support vector regression hyperparameters based on cross-validation
-
Portland: Institute of Electrical and Electronics Engineers Inc.
-
KENTARO I, RYOHEI N. Optimizing support vector regression hyperparameters based on cross-validation[C]//Proceedings of the International Joint Conference on Neural Networks. Portland: Institute of Electrical and Electronics Engineers Inc., 2003, 3: 2077-2082.
-
(2003)
Proceedings of the International Joint Conference on Neural Networks
, vol.3
, pp. 2077-2082
-
-
Kentaro, I.1
Ryohei, N.2
-
25
-
-
0036825788
-
Improved sparse least-squares support vector machines
-
CAWLEY G C, TALBOT N L C. Improved sparse least-squares support vector machines[J]. Neurocomputing, 2002, 48: 1025-1031.
-
(2002)
Neurocomputing
, vol.48
, pp. 1025-1031
-
-
Cawley, G.C.1
Talbot, N.L.C.2
-
26
-
-
15844392541
-
Fast bootstrap methodology for regression model selection
-
LENDASSE A, SIMON G, WERTZ V. Fast bootstrap methodology for regression model selection[J]. Neurocomputing, 2005, 64(1-4): 161-181.
-
(2005)
Neurocomputing
, vol.64
, Issue.1-4
, pp. 161-181
-
-
Lendasse, A.1
Simon, G.2
Wertz, V.3
-
27
-
-
0001025418
-
Bayesian interpolation
-
MACKAY D J C. Bayesian interpolation[J]. Neural Computation, 1992, 4(3): 415-447.
-
(1992)
Neural Computation
, vol.4
, Issue.3
, pp. 415-447
-
-
Mackay, D.J.C.1
-
28
-
-
0036161010
-
A probabilistic framework for SVM regression and error bar estimation
-
GAO J B, GUNN S R, HARRIS C J. A probabilistic framework for SVM regression and error bar estimation[J]. Machine Learning, 2002, 46: 71-89.
-
(2002)
Machine Learning
, vol.46
, pp. 71-89
-
-
Gao, J.B.1
Gunn, S.R.2
Harris, C.J.3
-
29
-
-
17444378757
-
Simple probabilistic predictions for support vector regression
-
Taipei: Department of Computer Science, National Taiwan University
-
LIN C J, WENG R C. Simple probabilistic predictions for support vector regression[R]. Taipei: Department of Computer Science, National Taiwan University, 2004.
-
(2004)
-
-
Lin, C.J.1
Weng, R.C.2
-
31
-
-
3442902601
-
Modeling method based on support vector machines within the Bayesian evidence framework
-
YAN Weiwu, CHANG Junlin, SHAO Huihe. Modeling method based on support vector machines within the Bayesian evidence framework[J]. Control and Decision, 2004, 19(5): 525-533.
-
(2004)
Control and Decision
, vol.19
, Issue.5
, pp. 525-533
-
-
Yan, W.1
Chang, J.2
Shao, H.3
-
32
-
-
1242331293
-
Bayesian support vector regression using a unified loss function
-
CHU W, KEERTHI S, ONG C J. Bayesian support vector regression using a unified loss function[J]. IEEE Trans. on Neural Networks, 2004, 15(1): 29-44.
-
(2004)
IEEE Trans. on Neural Networks
, vol.15
, Issue.1
, pp. 29-44
-
-
Chu, W.1
Keerthi, S.2
Ong, C.J.3
-
35
-
-
17444398555
-
Leave one out bounds for support vector regression model selection
-
CHANG M W, LIN C J. Leave one out bounds for support vector regression model selection[J]. Neural Computation, 2005, 17: 1188-1222.
-
(2005)
Neural Computation
, vol.17
, pp. 1188-1222
-
-
Chang, M.W.1
Lin, C.J.2
-
36
-
-
0242383459
-
SVM regression through variational methods and its sequential implementation
-
GAO J B, GUNN S R, HARRIS C J. SVM regression through variational methods and its sequential implementation[J]. Neurocomputing, 2003, 55(1-2): 151-167.
-
(2003)
Neurocomputing
, vol.55
, Issue.1-2
, pp. 151-167
-
-
Gao, J.B.1
Gunn, S.R.2
Harris, C.J.3
-
37
-
-
0345688978
-
Determination of the spread parameter in the Gaussian kernel for classification and regression
-
WANG Wenjian, XU Zongben, LU Weizhen, et al. Determination of the spread parameter in the Gaussian kernel for classification and regression[J]. Neurocomputing, 2003, 55(3-4): 643-663.
-
(2003)
Neurocomputing
, vol.55
, Issue.3-4
, pp. 643-663
-
-
Wang, W.1
Xu, Z.2
Lu, W.3
-
38
-
-
0346250790
-
Practical selection of SVM parameters and noise estimation for SVM regression
-
CHERKASSKY V, MA Y. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks, 2004, 17(1): 113-126.
-
(2004)
Neural Networks
, vol.17
, Issue.1
, pp. 113-126
-
-
Cherkassky, V.1
Ma, Y.2
-
39
-
-
33744538355
-
Hybrid approach of selecting hyperparameters of support vector machine for regression
-
JENG J T. Hybrid approach of selecting hyperparameters of support vector machine for regression[J]. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, 2006, 36(3): 699-709.
-
(2006)
IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics
, vol.36
, Issue.3
, pp. 699-709
-
-
Jeng, J.T.1
|