• Electronics Optics & Control
  • Vol. 20, Issue 6, 50 (2013)
LIU Luoxia
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.3969/j.issn.1671-637x.2013.06.012 Cite this Article
    LIU Luoxia. Choosing Multiple Parameters for Function Regression Based on SVM[J]. Electronics Optics & Control, 2013, 20(6): 50 Copy Citation Text show less
    References

    [1] VAPNIK V.Estimation of dependences based on empirical Data[M].New York:Springer Verlag1982.

    [2] BARTLETT P L.For valid generalizationthe size of the weights is more important than the size of the network[M]//MOZER M CJORDAN M IPetsche T.Advances in Neural Information Processing Systems. Cambridge MA:The MIT Press1997:134-140.

    [3] GEMAN SBINENESTOCK EDOURSAT R.Neural networks and the bias/variance dilemma[J].Neural Computation19924(1):1-58.

    [4] VAPNIK V.Statistical learning theory[M].New York:John Wiley and SonsInc1998.

    [5] BURGES C J C.A tutorial on support vector machine for pattern recognition[J].Data mining and knowledge discovery19982(2):134-140.

    [6] SMOLA A JSCHLKOPF B.A tutorial on support vector regression[D].NeuroCOLT2 technical report NC2-TR-1998-030.London:Royal Holloway College1998.

    [7] CRISTIANINI NSHAWE-TAYLOR J.An introduction to support vector machines and other kernel-based learning methods[M].Cambridge:Cambridge University Press 2000.

    [8] KECMAN V.Learning and soft computing:Support vector machinesneural networksand fuzzy logic models[M].CambridgeMA:The MIT Press2001.

    [9] CHAPELLE OVAPNIK VBOUSQUET Oet al.Choosing multiple parameters for support vector machine[J].Machine Learning200246(1/2/3):131-159.

    [10] GEN MCHENG R.Genetic algorithms and engineering design[M].New York:John Wiley & Sons1997.

    LIU Luoxia. Choosing Multiple Parameters for Function Regression Based on SVM[J]. Electronics Optics & Control, 2013, 20(6): 50
    Download Citation