메뉴 건너뛰기




Volumn 83, Issue 8 A, 2005, Pages 1030-1037

Regression models using pattern search assisted least square support vector machines

Author keywords

Equality constraints; LS SVM; Model selection; Optimization; Pattern search

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; REGRESSION ANALYSIS;

EID: 23944482252     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1205/cherd.03144     Document Type: Article
Times cited : (30)

References (42)
  • 1
    • 0031144409 scopus 로고    scopus 로고
    • Estimation of non-linear systems using linear multiple models
    • Banerjee, A., Arkun, Y., Ogunnaike, B. and Pearson, R., 1997, Estimation of non-linear systems using linear multiple models, AIChE J, 43(5): 1204-1226.
    • (1997) AIChE J. , vol.43 , Issue.5 , pp. 1204-1226
    • Banerjee, A.1    Arkun, Y.2    Ogunnaike, B.3    Pearson, R.4
  • 3
    • 0003408496 scopus 로고    scopus 로고
    • UCI Repository of machine learning databases
    • Department of Information and Computer Science, University of California, Irvine, CA, USA
    • Blake, C.L. and Merz, C.J., 1998, UCI Repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA, USA, http://www.ics.uci.edu/~mlearn/ MLRepository.html
    • (1998)
    • Blake, C.L.1    Merz, C.J.2
  • 4
    • 0003495934 scopus 로고
    • Technical Report 421, Department of Statistics, University of California, Berkley, USA
    • Breiman, L., 1994, Bagging Predictors, Technical Report 421, Department of Statistics, University of California, Berkley, USA.
    • (1994) Bagging Predictors
    • Breiman, L.1
  • 7
    • 84899013173 scopus 로고    scopus 로고
    • Support vector regression machines
    • Mozer, M.C., Jordan, M.I. and Petsche, T. (eds)., (MIT Press, Cambridge, MA, USA)
    • Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A. and Vapnik, V., 1997, Support vector regression machines, in Mozer, M.C., Jordan, M.I. and Petsche, T. (eds)., Advances in Neural Information Processing Systems, Vol. 9, 155-161 (MIT Press, Cambridge, MA, USA).
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 155-161
    • Drucker, H.1    Burges, C.J.C.2    Kaufman, L.3    Smola, A.4    Vapnik, V.5
  • 8
    • 0000251975 scopus 로고    scopus 로고
    • Neural network based quantitative structural property relations (QSPRs) for predicting boiling points of aliphatic hydrocarbons
    • Espinosa, G., Yaffe, D., Cohen, Y., Arenas, A. and Giralt, F., 2000, Neural network based quantitative structural property relations (QSPRs) for predicting boiling points of aliphatic hydrocarbons, J Chem Inf Comput Sci, 40: 859-879.
    • (2000) J. Chem. Inf. Comput. Sci. , vol.40 , pp. 859-879
    • Espinosa, G.1    Yaffe, D.2    Cohen, Y.3    Arenas, A.4    Giralt, F.5
  • 10
    • 0035890807 scopus 로고    scopus 로고
    • Evolutionary polymorphic neural network in chemical process modelling
    • Gao, L. and Loney, N.W., 2001, Evolutionary polymorphic neural network in chemical process modelling, Comp Chem Eng, 25(11-12): 1403-1410.
    • (2001) Comp. Chem. Eng. , vol.25 , Issue.11-12 , pp. 1403-1410
    • Gao, L.1    Loney, N.W.2
  • 11
    • 0001159626 scopus 로고    scopus 로고
    • Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model
    • Goll, E.S. and Jurs, P.C., 1999, Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model, J Chem Inf Comput Sci, 39: 974-983.
    • (1999) J. Chem. Inf. Comput. Sci. , vol.39 , pp. 974-983
    • Goll, E.S.1    Jurs, P.C.2
  • 12
    • 0004236492 scopus 로고
    • (Johns Hopkins University Press, Baltimore, MD, USA)
    • Golub, G.H. and Van Loan, C.F., 1989, Matrix Computations (Johns Hopkins University Press, Baltimore, MD, USA).
    • (1989) Matrix Computations
    • Golub, G.H.1    Van Loan, C.F.2
  • 13
    • 0003425664 scopus 로고    scopus 로고
    • Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton
    • Gunn, S.R., 1997, Support Vector Machines for Classification and Regression, Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton, http://www.ecs.soton.ac.uk/ ~srg/publications/pdf/SVM.pdf.
    • (1997) Support Vector Machines for Classification and Regression
    • Gunn, S.R.1
  • 14
    • 0017947982 scopus 로고
    • Hedonic housing prices and the demand for clean air
    • Harrison, D. and Rubinfeld, D.L., 1978, Hedonic housing prices and the demand for clean air, J Environ Econ Manag, 5: 81-102.
    • (1978) J. Environ. Econ. Manag. , vol.5 , pp. 81-102
    • Harrison, D.1    Rubinfeld, D.L.2
  • 15
    • 0000135303 scopus 로고
    • Methods of conjugate gradients for solving linear systems
    • Hestenes, M.R. and Stiefel, E., 1952, Methods of conjugate gradients for solving linear systems, J Res Natl Bur Stand, 49: 409-436.
    • (1952) J. Res. Natl. Bur. Stand. , vol.49 , pp. 409-436
    • Hestenes, M.R.1    Stiefel, E.2
  • 16
    • 0024880831 scopus 로고
    • Multilayer feed forward networks are universal approximators
    • Hornik, K., Stinchcombe, M. and White, H., 1989, Multilayer feed forward networks are universal approximators, Neural Networks, 2: 359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 18
    • 0001246549 scopus 로고
    • Higher chaos in a four variable chemical reaction model
    • Killory, H., Rössler, O.E. and Hudson, J.L., 1987, Higher chaos in a four variable chemical reaction model, Physics Letters A, 122: 341-345.
    • (1987) Physics Letters A , vol.122 , pp. 341-345
    • Killory, H.1    Rössler, O.E.2    Hudson, J.L.3
  • 19
    • 0033412824 scopus 로고    scopus 로고
    • Pattern search algorithms for bound constrained minimization
    • Lewis, R. and Torczon, V., 1996, Pattern search algorithms for bound constrained minimization, SIAM J Optimization, 9: 1082-1099.
    • (1996) SIAM J. Optimization , vol.9 , pp. 1082-1099
    • Lewis, R.1    Torczon, V.2
  • 20
    • 0040888006 scopus 로고    scopus 로고
    • A new efficient approach for variable selection based on multiregression: Prediction of gas chromatographic retention times and response factors
    • Lucic, B. and Trinajstic, N., 1999, A new efficient approach for variable selection based on multiregression: prediction of gas chromatographic retention times and response factors, J Chem Inf Comput Sci, 39: 610-621.
    • (1999) J. Chem. Inf. Comput. Sci. , vol.39 , pp. 610-621
    • Lucic, B.1    Trinajstic, N.2
  • 21
    • 0000104192 scopus 로고    scopus 로고
    • Nonlinear multivariate regression outperforms several concisely designed neural networks on three QSPR data sets
    • Lucic, B., Amic, D. and Trinajstic, N., 2000, Nonlinear multivariate regression outperforms several concisely designed neural networks on three QSPR data sets, J Chem Inf Comput Sci, 40: 403-413.
    • (2000) J. Chem. Inf. Comput. Sci. , vol.40 , pp. 403-413
    • Lucic, B.1    Amic, D.2    Trinajstic, N.3
  • 22
    • 0017714604 scopus 로고
    • Oscillation and chaos in physiological control systems
    • Mackey, M. and Glass, L., 1977, Oscillation and chaos in physiological control systems, Science, 197: 287-289.
    • (1977) Science , vol.197 , pp. 287-289
    • Mackey, M.1    Glass, L.2
  • 24
    • 0242288791 scopus 로고    scopus 로고
    • N4: Computing with neural receptive fields
    • Pedrycz, W., Chun, M.G. and Succi, G., 2003, N4: computing with neural receptive fields, Neurocomputing, 55: 383-401.
    • (2003) Neurocomputing , vol.55 , pp. 383-401
    • Pedrycz, W.1    Chun, M.G.2    Succi, G.3
  • 25
    • 0025490985 scopus 로고
    • Networks for approximation and learning
    • Poggio, T. and Girosi, F., 1990, Networks for approximation and learning, Proceedings of IEEE, 78(9): 1481-1497.
    • (1990) Proceedings of IEEE , vol.78 , Issue.9 , pp. 1481-1497
    • Poggio, T.1    Girosi, F.2
  • 26
    • 0035561486 scopus 로고    scopus 로고
    • Kernel PCA for feature extraction and de-noising nonlinear regression
    • Rosipal, R., Girolami, K., Trejo, L.J. and Cichocki, A., 2001, Kernel PCA for feature extraction and de-noising nonlinear regression, Neural Comput Applic, 10: 231-243.
    • (2001) Neural. Comput. Applic. , vol.10 , pp. 231-243
    • Rosipal, R.1    Girolami, K.2    Trejo, L.J.3    Cichocki, A.4
  • 29
    • 0004240479 scopus 로고    scopus 로고
    • Learning with kernels
    • Ph.D. thesis, GMD, Birlinghoven, Germany
    • Smola, A., 1999, Learning with kernels, Ph.D. thesis, GMD, Birlinghoven, Germany.
    • (1999)
    • Smola, A.1
  • 30
    • 0003401675 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • NeuroCOLT2 Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK
    • Smola, A. and Schölkopf, B., 1998, A tutorial on support vector regression, NeuroCOLT2 Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK.
    • (1998)
    • Smola, A.1    Schölkopf, B.2
  • 32
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • Suykens, J.A.K. and Vandewalle, J., 1999, Least squares support vector machine classifiers, Neural Processing Letters, 9(3): 293-300.
    • (1999) Neural Processing Letters , vol.9 , Issue.3 , pp. 293-300
    • Suykens, J.A.K.1    Vandewalle, J.2
  • 35
    • 0036825528 scopus 로고    scopus 로고
    • Weighted least squares support vector machines: Robustness and sparse approximation
    • Suykens, J.A.K., De Brabanter, J., Lukas, L. and Vandewalle, J. 2002a, Weighted least squares support vector machines: robustness and sparse approximation, Neurocomputing, 48: 85-105.
    • (2002) Neurocomputing , vol.48 , pp. 85-105
    • Suykens, J.A.K.1    De Brabanter, J.2    Lukas, L.3    Vandewalle, J.4
  • 38
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • Mozer, M.C., Jordan, M.I. and Petsche, T. (eds), (MIT Press, Cambridge, MA, USA)
    • Vapnik, V., Golowich, S. and Smola, A., 1997, Support vector method for function approximation, regression estimation, and signal processing, in Mozer, M.C., Jordan, M.I. and Petsche, T. (eds), Advances in Neural Information Processing Systems, Vol. 9, 281-287 (MIT Press, Cambridge, MA, USA).
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 281-287
    • Vapnik, V.1    Golowich, S.2    Smola, A.3
  • 39
    • 0037443771 scopus 로고    scopus 로고
    • A review of process fault detection and diagnosis. Part II: Qualitative models and search strategies
    • Venkatasubramanian, V., Rengaswamy, R. and Kavuri, S.N., 2003, A review of process fault detection and diagnosis. Part II: Qualitative models and search strategies, Comp Chem Eng, 27(3): 313-326.
    • (2003) Comp. Chem. Eng. , vol.27 , Issue.3 , pp. 313-326
    • Venkatasubramanian, V.1    Rengaswamy, R.2    Kavuri, S.N.3
  • 40
    • 33644770673 scopus 로고    scopus 로고
    • Spider v1.6: An object oriented machine learning library
    • Weston, J., Elisseeff, A., Bakir, G. and F. Sinz, 2005, Spider v1.6: an object oriented machine learning library, http://www.kyb.tuebingen.mpg. de/bs/people/spider/index.html.
    • (2005)
    • Weston, J.1    Elisseeff, A.2    Bakir, G.3    Sinz, F.4
  • 41
    • 0033495144 scopus 로고    scopus 로고
    • Multi-stage modeling of a semi-batch polymerization reactor using artificial neural networks
    • Yang, S.H., Chung, P.W.H. and Brooks, B.W., 1999, Multi-stage modeling of a semi-batch polymerization reactor using artificial neural networks, Trans IChemE, Part A, Chem Eng Res Des, 77(8): 779-783.
    • (1999) Trans. IChemE, Part A, Chem. Eng. Res. Des. , vol.77 , Issue.8 , pp. 779-783
    • Yang, S.H.1    Chung, P.W.H.2    Brooks, B.W.3
  • 42
    • 0035443180 scopus 로고    scopus 로고
    • Composition estimations in a middle-vessel batch distillation column using artificial neural networks
    • Zamprogna, E., Barolo M. and Seborg, D.E., 2001, Composition estimations in a middle-vessel batch distillation column using artificial neural networks, Trans IChemE, Part A, Chem Eng Res Des, 79: 689-696.
    • (2001) Trans. IChemE, Part A, Chem. Eng. Res. Des. , vol.79 , pp. 689-696
    • Zamprogna, E.1    Barolo, M.2    Seborg, D.E.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.