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Volumn 15, Issue 11, 2003, Pages 2683-2703

Accurate On-line Support Vector Regression

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; REGRESSION ANALYSIS;

EID: 0141765796     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603322385117     Document Type: Article
Times cited : (408)

References (32)
  • 1
    • 80052866161 scopus 로고    scopus 로고
    • Incremental and decremental support vector machine learning
    • T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Cambridge, MA: MIT Press
    • Cauwenberghs, G., & Poggio, T. (2001). Incremental and decremental support vector machine learning. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, 13 (pp. 409-123). Cambridge, MA: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 409-1123
    • Cauwenberghs, G.1    Poggio, T.2
  • 3
    • 0038895405 scopus 로고    scopus 로고
    • Training ν-support vector regression: Theory and algorithms
    • Chang, C.-C., & Lin, C.-J. (2002). Training ν-support vector regression: Theory and algorithms. Neural Computation, 14, 1959-1977.
    • (2002) Neural Computation , vol.14 , pp. 1959-1977
    • Chang, C.-C.1    Lin, C.-J.2
  • 4
    • 84898947911 scopus 로고    scopus 로고
    • Sparse representation for gaussian process models
    • T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Cambridge, MA: MIT Press
    • Csato, L., & Opper, M. (2001). Sparse representation for gaussian process models. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, 13 (pp. 444-450). Cambridge, MA: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 444-450
    • Csato, L.1    Opper, M.2
  • 5
    • 33747180475 scopus 로고    scopus 로고
    • Predicting time series with a local support vector regression machine
    • Fernández, R. (1999). Predicting time series with a local support vector regression machine. In Advanced Course on Artificial Intelligence (ACAI '99). Available on-line at: http://www.iit.demokritos.gr/skel/eetn/acai99/.
    • (1999) Advanced Course on Artificial Intelligence (ACAI '99)
    • Fernández, R.1
  • 8
    • 84868111801 scopus 로고    scopus 로고
    • A new approximate maximal margin classification algorithm
    • Gentile, C. (2001). A new approximate maximal margin classification algorithm. Journal of Machine Learning Research, 2, 213-242.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 213-242
    • Gentile, C.1
  • 9
    • 0000737575 scopus 로고    scopus 로고
    • Warm start of the primal-dual method applied in the cutting plane scheme
    • Gondzio, J., (1998). Warm start of the primal-dual method applied in the cutting plane scheme. Mathematical Programming, 83, 125-143.
    • (1998) Mathematical Programming , vol.83 , pp. 125-143
    • Gondzio, J.1
  • 10
    • 0042659381 scopus 로고    scopus 로고
    • Reoptimization with the primal-dual interior point method
    • Gondzio, J., & Grothey, A. (2001). Reoptimization with the primal-dual interior point method. SIAM Journal on Optimization, 13, 842-864.
    • (2001) SIAM Journal on Optimization , vol.13 , pp. 842-864
    • Gondzio, J.1    Grothey, A.2
  • 11
    • 84898991622 scopus 로고    scopus 로고
    • From margin to sparsity
    • T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Cambridge, MA: MIT Press
    • Graepel, T., Herbrich, R., & Williamson, R. C. (2001). From margin to sparsity. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, 13 (pp. 210-216). Cambridge, MA: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 210-216
    • Graepel, T.1    Herbrich, R.2    Williamson, R.C.3
  • 12
    • 0003417806 scopus 로고    scopus 로고
    • Software
    • Gunn, S. (1998). Matlab SVM toolbox. Software. Available on-line at: http:// www.isis.ecs.soton.ac.uk/resources/svminfo/.
    • (1998) Matlab SVM Toolbox
    • Gunn, S.1
  • 13
    • 84943226219 scopus 로고    scopus 로고
    • Learning additive models online with fast evaluating kernels
    • D. P. Helmbold & B. Williamson (Eds.), New York: Springer-Verlag
    • Herbster, M. (2001). Learning additive models online with fast evaluating kernels. In D. P. Helmbold & B. Williamson (Eds.), Proceedings of the 14th Annual Conference on Computational Learning Theory (pp. 444-460). New York: Springer-Verlag.
    • (2001) Proceedings of the 14th Annual Conference on Computational Learning Theory , pp. 444-460
    • Herbster, M.1
  • 14
    • 0003307180 scopus 로고    scopus 로고
    • Estimating the generalization performance of an SVM efficiently
    • P. Langley (Ed.), San Mateo: Morgan Kaufmann
    • Joachims, T. (2000). Estimating the generalization performance of an SVM efficiently. In P. Langley (Ed.), Proceedings of the Seventeenth International Conference on Machine Learning (pp. 431-438). San Mateo: Morgan Kaufmann.
    • (2000) Proceedings of the Seventeenth International Conference on Machine Learning , pp. 431-438
    • Joachims, T.1
  • 15
    • 84898940321 scopus 로고    scopus 로고
    • Online learning with kernels
    • T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Cambridge, MA: MIT Press
    • Kivinen, J., Smola, A. J., & Williamson, R. C. (2002). Online learning with kernels. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14 (pp. 785-792). Cambridge, MA: MIT Press.
    • (2002) Advances in Neural Information Processing Systems , vol.14 , pp. 785-792
    • Kivinen, J.1    Smola, A.J.2    Williamson, R.C.3
  • 17
    • 84898964646 scopus 로고    scopus 로고
    • The relaxed online maximum margin algorithm
    • S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Cambridge, MA: MIT Press
    • Li, Y., & Long, P. M. (1999). The relaxed online maximum margin algorithm. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in neural information processing systems, 12 (pp. 498-504). Cambridge, MA: MIT Press.
    • (1999) Advances in Neural Information Processing Systems , vol.12 , pp. 498-504
    • Li, Y.1    Long, P.M.2
  • 18
    • 0017714604 scopus 로고
    • Oscillation and chaos in physiological control systems
    • Mackey, M. C., & Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197, 287-289.
    • (1977) Science , vol.197 , pp. 287-289
    • Mackey, M.C.1    Glass, L.2
  • 19
    • 0141556297 scopus 로고    scopus 로고
    • Tech. Rep. LSI-02-11-R. Catalunya, Spain: Software Department, Universitat Politecnica de Catalunya
    • Martin, M. (2002). On-line support vector machines for function approximation. (Tech. Rep. LSI-02-11-R).Catalunya, Spain: Software Department, Universitat Politecnica de Catalunya.
    • (2002) On-Line Support Vector Machines for Function Approximation
    • Martin, M.1
  • 21
    • 84958962423 scopus 로고    scopus 로고
    • Incremental support vector machine learning: A local approach
    • G. Dorffner, H. Bischof, & K. Hornik (Eds.), Berlin: Springer-Verlag
    • Ralaivola, L., & d'Alche-Buc, F. (2001). Incremental support vector machine learning: A local approach. In G. Dorffner, H. Bischof, & K. Hornik (Eds.), Artificial Neural Networks - ICANN 2001 (pp. 322-330) Berlin: Springer-Verlag.
    • (2001) Artificial Neural Networks - ICANN 2001 , pp. 322-330
    • Ralaivola, L.1    D'Alche-Buc, F.2
  • 24
    • 0003401675 scopus 로고    scopus 로고
    • NeuroCOLT Tech. Rep. No. NC-TR-98-030. London: Royal Holloway College, University of London.
    • Smola, A. J., & Schölkopf, B. (1998). A tutorial on support vector regression. (NeuroCOLT Tech. Rep. No. NC-TR-98-030). London: Royal Holloway College, University of London.
    • (1998) A Tutorial on Support Vector Regression
    • Smola, A.J.1    Schölkopf, B.2
  • 26
    • 0034288853 scopus 로고    scopus 로고
    • Out-of-sample tests of forecasting accuracy: An analysis and review
    • Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting, 16, 437-450.
    • (2000) International Journal of Forecasting , vol.16 , pp. 437-450
    • Tashman, L.J.1
  • 27
    • 0001023715 scopus 로고    scopus 로고
    • Application of support vector machines in financial time series forcasting
    • Tay, F. E. H., & Cao, L. (2001). Application of support vector machines in financial time series forcasting. Omega, 29, 309-317.
    • (2001) Omega , vol.29 , pp. 309-317
    • Tay, F.E.H.1    Cao, L.2
  • 28
    • 0033293912 scopus 로고    scopus 로고
    • LOQO: An interior point code for quadratic programming
    • Vanderbei, R. J. (1999). LOQO: An interior point code for quadratic programming. Optimization Methods and Software, 11, 451-484.
    • (1999) Optimization Methods and Software , vol.11 , pp. 451-484
    • Vanderbei, R.J.1
  • 30
    • 0038928834 scopus 로고    scopus 로고
    • Bounds on error expectation for support vector machine
    • A. Smola, P. Bartlett, B. Schölkopf, & D. Schuurmans (Eds.), Cambridge, MA: MIT Press
    • Vapnik, V., & Chapelle, O. (1999). Bounds on error expectation for support vector machine. In A. Smola, P. Bartlett, B. Schölkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers (pp. 261-280). Cambridge, MA: MIT Press.
    • (1999) Advances in Large Margin Classifiers , pp. 261-280
    • Vapnik, V.1    Chapelle, O.2
  • 32
    • 0036354771 scopus 로고    scopus 로고
    • Warm-start strategies in interior-point methods for linear programming
    • Yildirim, E. A., & Wright, S. J. (2002). Warm-start strategies in interior-point methods for linear programming. SIAM Journal on Optimization, 12, 782-810.
    • (2002) SIAM Journal on Optimization , vol.12 , pp. 782-810
    • Yildirim, E.A.1    Wright, S.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.