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Volumn 4, Issue , 2004, Pages 3388-3393

Partially linear models and least squares support vector machines

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; MONTE CARLO METHODS; NONLINEAR CONTROL SYSTEMS; PARAMETER ESTIMATION; QUADRATIC PROGRAMMING; REGRESSION ANALYSIS; SET THEORY;

EID: 14244268632     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/cdc.2004.1429230     Document Type: Conference Paper
Times cited : (19)

References (21)
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    • Linear models of nonlinear FIR systems with gaussian inputs
    • Linköping Universitet, Sweden
    • M. Enqvist. Linear Models of Nonlinear FIR Systems with Gaussian Inputs. Technical Report LiTH-ISY-R-2462, Linköping Universitet, Sweden, 2002.
    • (2002) Technical Report , vol.LITH-ISY-R-2462
    • Enqvist, M.1
  • 4
    • 14244249639 scopus 로고    scopus 로고
    • NARX identification of hammerstein models using least squares support vector machines
    • ESAT-SISTA, K.U.Leuven
    • I. Goethals, K. Pelckmans, J.A.K. Suykens, and B. De Moor. NARX Identification of Hammerstein Models Using Least Squares Support Vector Machines. Technical Report Internal Report 04-40, ESAT-SISTA, K.U.Leuven, 2004.
    • (2004) Technical Report Internal Report , vol.4 , Issue.40
    • Goethals, I.1    Pelckmans, K.2    Suykens, J.A.K.3    De Moor, B.4
  • 6
    • 0003428336 scopus 로고
    • Econometric Society Monographs. Cambridge University Press
    • W. Härdle. Applied Nonparametric Regression. Econometric Society Monographs. Cambridge University Press, 1989.
    • (1989) Applied Nonparametric Regression
    • Härdle, W.1
  • 10
    • 0000597408 scopus 로고    scopus 로고
    • Comparison of approximate methods for handling hyperparameters
    • D.J.C. MacKay. Comparison of approximate methods for handling hyperparameters. Neural Computation, 11:1035-1068, 1999.
    • (1999) Neural Computation , vol.11 , pp. 1035-1068
    • MacKay, D.J.C.1
  • 12
    • 0025490985 scopus 로고
    • Networks for approximation and learning
    • T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of the IEEE, 78:1481-1497, 1990.
    • (1990) Proceedings of the IEEE , vol.78 , pp. 1481-1497
    • Poggio, T.1    Girosi, F.2
  • 13
    • 0000218602 scopus 로고
    • Root n-consistent semiparametric regression
    • P.M. Robinson. Root n-consistent Semiparametric Regression. Econometrica, 56(4):931-954, 1988.
    • (1988) Econometrica , vol.56 , Issue.4 , pp. 931-954
    • Robinson, P.M.1
  • 16
    • 84898946392 scopus 로고    scopus 로고
    • Semiparametric support vector and linear programming machines
    • M.S. Kearns, S.A. Solla, and D.A. Cohn, editors. MIT Press
    • A.J. Smola, T. Friess, and B. Schölkopf. Semiparametric support vector and linear programming machines. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 585-591. MIT Press, 1999.
    • (1999) Advances in Neural Information Processing Systems , vol.11 , pp. 585-591
    • Smola, A.J.1    Friess, T.2    Schölkopf, B.3
  • 18
    • 0036825528 scopus 로고    scopus 로고
    • Weighted least squares support vector machines: Robustness and sparse approximation
    • J.A.K. Suykens, J. De Brabanter, L. Lukas, and J. Vandewalle. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1-4):85-105, 2002.
    • (2002) Neurocomputing , vol.48 , Issue.1-4 , pp. 85-105
    • Suykens, J.A.K.1    De Brabanter, J.2    Lukas, L.3    Vandewalle, J.4
  • 20


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.