메뉴 건너뛰기




Volumn 73, Issue 1-3, 2009, Pages 495-505

Weighted solution path algorithm of support vector regression based on heuristic weight-setting optimization

Author keywords

Keywords: Support vector machines; Particle swarm optimization; Solution path; Weight setting

Indexed keywords

ARC-TANGENTS; FUNCTION REGRESSION; GENERALIZATION ABILITY; KEYWORDS: SUPPORT VECTOR MACHINES; ON TIME; OPTIMAL WEIGHT; OPTIMIZATION ALGORITHMS; PENALTY PARAMETERS; SOLUTION PATH; SUPPORT VECTOR REGRESSIONS; TIME SERIES PREDICTION; TRAINING SAMPLE; WEIGHT-SETTING;

EID: 70350714263     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.06.008     Document Type: Article
Times cited : (7)

References (41)
  • 4
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • Cherkassky V., and Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 (2004) 113-126
    • (2004) Neural Networks , vol.17 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 5
    • 0036464756 scopus 로고    scopus 로고
    • The particle swarm-explosion, stability, and convergence in amultidimensional complex space
    • Clerc M., and Kennedy J. The particle swarm-explosion, stability, and convergence in amultidimensional complex space. IEEE Trans. Evol. Comput. 6 1 (2002) 58-73
    • (2002) IEEE Trans. Evol. Comput. , vol.6 , Issue.1 , pp. 58-73
    • Clerc, M.1    Kennedy, J.2
  • 6
    • 34250263445 scopus 로고
    • Smoothing noisy data with spline function
    • Craven P., and Wahba G. Smoothing noisy data with spline function. Numer. Math. 31 4 (1979) 377-403
    • (1979) Numer. Math. , vol.31 , Issue.4 , pp. 377-403
    • Craven, P.1    Wahba, G.2
  • 8
    • 0002432565 scopus 로고
    • Multivariate adaptive regression splines
    • Friedman J.H. Multivariate adaptive regression splines. Ann. Statist. 19 1 (1991) 1-67
    • (1991) Ann. Statist. , vol.19 , Issue.1 , pp. 1-67
    • Friedman, J.H.1
  • 9
    • 15844394276 scopus 로고    scopus 로고
    • Evolutionary tuning of multiple SVM parameters
    • Friedrichs F., and Igel C. Evolutionary tuning of multiple SVM parameters. Neurocomputing 64 (2005) 107-117
    • (2005) Neurocomputing , vol.64 , pp. 107-117
    • Friedrichs, F.1    Igel, C.2
  • 10
    • 33847321021 scopus 로고    scopus 로고
    • Fuzzy prediction of chaotic time series based on singular value decomposition
    • Gu H., and Wang H.W. Fuzzy prediction of chaotic time series based on singular value decomposition. Appl. Math. Comput. 185 2 (2007) 1171-1185
    • (2007) Appl. Math. Comput. , vol.185 , Issue.2 , pp. 1171-1185
    • Gu, H.1    Wang, H.W.2
  • 11
    • 34249726632 scopus 로고    scopus 로고
    • Efficient computation and model selection for the support vector regression
    • Gunter L., and Zhu J. Efficient computation and model selection for the support vector regression. Neural Comput. 19 (2007) 1633-1655
    • (2007) Neural Comput. , vol.19 , pp. 1633-1655
    • Gunter, L.1    Zhu, J.2
  • 12
    • 56549111881 scopus 로고    scopus 로고
    • A novel LS-SVMs hyper-parameter selection based on particle swarm optimization
    • Guo X.C., Yang J.H., Wu C.G., et al. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing 71 (2008) 3211-3215
    • (2008) Neurocomputing , vol.71 , pp. 3211-3215
    • Guo, X.C.1    Yang, J.H.2    Wu, C.G.3
  • 13
    • 33748424239 scopus 로고    scopus 로고
    • The effect of different basis functions on a radial basis function network for time series prediction: a comparative study
    • Harpham C., and Dawson C.W. The effect of different basis functions on a radial basis function network for time series prediction: a comparative study. Neurocomputing 69 (2006) 2161-2170
    • (2006) Neurocomputing , vol.69 , pp. 2161-2170
    • Harpham, C.1    Dawson, C.W.2
  • 14
    • 84925605946 scopus 로고    scopus 로고
    • The entire regularization path for the support vector machine
    • Hastie T., Rosset S., Tibshirani R., and Zhu J. The entire regularization path for the support vector machine. J. Mach. Learn. Res. 5 (2004) 1391-1415
    • (2004) J. Mach. Learn. Res. , vol.5 , pp. 1391-1415
    • Hastie, T.1    Rosset, S.2    Tibshirani, R.3    Zhu, J.4
  • 15
    • 0036738840 scopus 로고    scopus 로고
    • Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
    • Keerthi S. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans. Neural Networks 13 5 (2002) 1225-1229
    • (2002) IEEE Trans. Neural Networks , vol.13 , Issue.5 , pp. 1225-1229
    • Keerthi, S.1
  • 17
    • 84958987386 scopus 로고    scopus 로고
    • Linear dependency between epsilon and the input noise in epsilon-support vector regression
    • Proceedings of the International Conference on Artificial Neural Networks, Springer, London, UK
    • J.T. Kwok, Linear dependency between epsilon and the input noise in epsilon-support vector regression, in: Proceedings of the International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, vol. 2130, Springer, London, UK, 2001, pp. 405-410.
    • (2001) Lecture Notes in Computer Science , vol.2130 , pp. 405-410
    • Kwok, J.T.1
  • 18
    • 38349186243 scopus 로고    scopus 로고
    • Local prediction of non-linear time series using support vector regression
    • Lau K.W., and Wu Q.H. Local prediction of non-linear time series using support vector regression. Pattern Recognition 41 5 (2008) 1539-1547
    • (2008) Pattern Recognition , vol.41 , Issue.5 , pp. 1539-1547
    • Lau, K.W.1    Wu, Q.H.2
  • 19
  • 20
    • 20444401917 scopus 로고    scopus 로고
    • Noise robust estimates of the largest Lyapunov exponent
    • Liu H.F., Dai Z.H., Li W.F., Gong X., and Yu Z.H. Noise robust estimates of the largest Lyapunov exponent. Phys. Lett. A 341 (2005) 119-127
    • (2005) Phys. Lett. A , vol.341 , pp. 119-127
    • Liu, H.F.1    Dai, Z.H.2    Li, W.F.3    Gong, X.4    Yu, Z.H.5
  • 21
    • 38049143933 scopus 로고    scopus 로고
    • Regularization paths for ν-SVM and ν-SVR
    • D. Liu, S. Fei, Z.G. Hou, H. Zhang, C.Y. Sun Eds, Advances in Neural Networks-ISNN, Springer, Berlin
    • G. Loosli, G. Gasso, S. Canu, Regularization paths for ν-SVM and ν-SVR, in: D. Liu, S. Fei, Z.G. Hou, H. Zhang, C.Y. Sun (Eds.), Advances in Neural Networks-ISNN, Lecture Notes in Computer Science, vol. 4493, Springer, Berlin, 2007, pp. 486-496.
    • (2007) Lecture Notes in Computer Science , vol.4493 , pp. 486-496
    • Loosli, G.1    Gasso, G.2    Canu, S.3
  • 22
    • 0000241853 scopus 로고
    • Deterministic nonperiodic flow
    • Lorenz E. Deterministic nonperiodic flow. J. Atmos. Sci. 20 (1963) 130
    • (1963) J. Atmos. Sci. , vol.20 , pp. 130
    • Lorenz, E.1
  • 23
    • 0031375732 scopus 로고    scopus 로고
    • S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using support vector machines, in: Proceedings of the IEEE Workshop on Neural Network for Signal Processing (NNSP'97), IEEE Press, Amelia Island, FL, 1997, pp. 511-520.
    • S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using support vector machines, in: Proceedings of the IEEE Workshop on Neural Network for Signal Processing (NNSP'97), IEEE Press, Amelia Island, FL, 1997, pp. 511-520.
  • 24
    • 84956628443 scopus 로고    scopus 로고
    • K.R. Müller, A.J. Smola, G. Rätsch, B. Schölkopf et al., Predicting time series with support vector machines, in: Proceedings of International Conference on Artificial Neural Networks, 1997, Lausanne, Switzerland, pp. 999-1004.
    • K.R. Müller, A.J. Smola, G. Rätsch, B. Schölkopf et al., Predicting time series with support vector machines, in: Proceedings of International Conference on Artificial Neural Networks, 1997, Lausanne, Switzerland, pp. 999-1004.
  • 26
    • 34548452938 scopus 로고    scopus 로고
    • Piecewise linear regularized solution paths
    • Rosset S., and Zhu J. Piecewise linear regularized solution paths. Ann. Statist. 35 3 (2007) 1012-1030
    • (2007) Ann. Statist. , vol.35 , Issue.3 , pp. 1012-1030
    • Rosset, S.1    Zhu, J.2
  • 30
    • 0003401675 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Royal Holloway College, London, UK
    • A.J. Smola, B. Schölkopf, A tutorial on support vector regression, NeuroCOLT Technical Report TR, Royal Holloway College, London, UK, 1998.
    • (1998) NeuroCOLT Technical Report TR
    • Smola, A.J.1    Schölkopf, B.2
  • 31
    • 0000169918 scopus 로고
    • Estimation of the mean of a multivariate normal distribution
    • Stein C. Estimation of the mean of a multivariate normal distribution. Ann. Statist. 9 6 (1981) 1135-1151
    • (1981) Ann. Statist. , vol.9 , Issue.6 , pp. 1135-1151
    • Stein, C.1
  • 32
    • 0036825528 scopus 로고    scopus 로고
    • Weighted least squares support vector machines: robustness and sparse approximation
    • Suykens J.A.K., Brabanter J.D., Lukas L., and Vandewalle J. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48 (2002) 85-105
    • (2002) Neurocomputing , vol.48 , pp. 85-105
    • Suykens, J.A.K.1    Brabanter, J.D.2    Lukas, L.3    Vandewalle, J.4
  • 33
    • 0036825901 scopus 로고    scopus 로고
    • Modified support vector machines in financial time series forecasting
    • Tay F.E.H., and Cao L.J. Modified support vector machines in financial time series forecasting. Neurocomputing 48 (2002) 847-861
    • (2002) Neurocomputing , vol.48 , pp. 847-861
    • Tay, F.E.H.1    Cao, L.J.2
  • 35
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • Mozer M., Jordan M., and Petsche T. (Eds), MIT Press, Cambridge
    • Vapnik V.N., Golowich S., and Smola A.J. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M., Jordan M., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), MIT Press, Cambridge 281-287
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 281-287
    • Vapnik, V.N.1    Golowich, S.2    Smola, A.J.3
  • 36
    • 54349106864 scopus 로고    scopus 로고
    • A new solution path algorithm in support vector regression
    • G. Wang, D.Y. Yeung, F.H. Lochovsky, A new solution path algorithm in support vector regression, IEEE Trans. Neural Networks, 19 (10) (2008) 1753-1767
    • (2008) IEEE Trans. Neural Networks , vol.19 , Issue.10 , pp. 1753-1767
    • Wang, G.1    Yeung, D.Y.2    Lochovsky, F.H.3
  • 37
    • 34250767233 scopus 로고    scopus 로고
    • Two dimensional solution path for support vector regression
    • Proceedings of the 23rd International Conference on Machine Learning ICML, Pittsburgh, Pennsylvania
    • G. Wang, D.Y. Yeung, F.H. Lochovsky, Two dimensional solution path for support vector regression, in: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), ACM International Conference Proceeding Series, Pittsburgh, Pennsylvania, 2006, pp. 993-1000.
    • (2006) ACM International Conference Proceeding Series , pp. 993-1000
    • Wang, G.1    Yeung, D.Y.2    Lochovsky, F.H.3
  • 38
    • 56549109169 scopus 로고    scopus 로고
    • A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression
    • Wen W., Hao Z.F., and Yang X.W. A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression. Neurocomputing 71 (2008) 3096-3103
    • (2008) Neurocomputing , vol.71 , pp. 3096-3103
    • Wen, W.1    Hao, Z.F.2    Yang, X.W.3
  • 40
    • 23044501770 scopus 로고    scopus 로고
    • Reweighted robust support vector regression method
    • Zhang J.S., and Gao G. Reweighted robust support vector regression method. Chin. J. Comput. Sci. 28 7 (2005) 1171-1177
    • (2005) Chin. J. Comput. Sci. , vol.28 , Issue.7 , pp. 1171-1177
    • Zhang, J.S.1    Gao, G.2
  • 41
    • 84899024917 scopus 로고    scopus 로고
    • 1-norm support vector machines
    • Thrun S., Saul L.K., and Schölkopf B. (Eds), MIT Press, Cambridge
    • Zhu J., Rosset S., Hastie T., and Tibshirani R. 1-norm support vector machines. In: Thrun S., Saul L.K., and Schölkopf B. (Eds). Advances in Neural Information Processing Systems vol. 16 (2004), MIT Press, Cambridge 49-56
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 49-56
    • Zhu, J.1    Rosset, S.2    Hastie, T.3    Tibshirani, R.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.