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Volumn 2005, Issue , 2005, Pages 5716-5721

Imposing symmetry in least squares support vector machines regression

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; CONSTRAINT THEORY; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

EID: 33847214167     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2005.1583074     Document Type: Conference Paper
Times cited : (19)

References (18)
  • 1
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    • Constraining the topology of neural networks to ensure dynamics with symmetry properties
    • L.A. Aguirre, R. Lopes, G. Amaral, and C. Letellier. Constraining the topology of neural networks to ensure dynamics with symmetry properties. Physical Review E, 69, 2004.
    • (2004) Physical Review E , vol.69
    • Aguirre, L.A.1    Lopes, R.2    Amaral, G.3    Letellier, C.4
  • 5
    • 33847204737 scopus 로고    scopus 로고
    • M. Espinoza, J.A.K. Suykens, and B. De Moor. Model structure determination and identification with kernel based partially linear models. Technical Report 04-110, ESAT-SCD-SISTA, K.U.Leuven, Belgium, 2004.
    • M. Espinoza, J.A.K. Suykens, and B. De Moor. Model structure determination and identification with kernel based partially linear models. Technical Report 04-110, ESAT-SCD-SISTA, K.U.Leuven, Belgium, 2004.
  • 6
    • 0038259114 scopus 로고    scopus 로고
    • Classes of kernel for machine learning: A statistics perspective
    • M. Genton. Classes of kernel for machine learning: A statistics perspective. Journal of Machine Learning Research, 2:299-312, 2001.
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 299-312
    • Genton, M.1
  • 7
    • 0030109138 scopus 로고    scopus 로고
    • Identification of non-linear systems using empirical data and prior knowledge-an optimization approach
    • T. Johansen. Identification of non-linear systems using empirical data and prior knowledge-an optimization approach. Automatica, 32(3):337-356, 1996.
    • (1996) Automatica , vol.32 , Issue.3 , pp. 337-356
    • Johansen, T.1
  • 8
    • 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
  • 9
    • 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
  • 12
    • 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
  • 14


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.