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Volumn , Issue , 2009, Pages 907-915

Multiple incremental decremental learning of support vector machines

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[No Author keywords available]

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EID: 80053620586     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (47)

References (15)
  • 1
    • 80052866161 scopus 로고    scopus 로고
    • Incremental and decremental support vector machine learning
    • (T. K. Leen, T. G. Dietterich, and V. Tresp, eds.) (Cambridge, Massachussetts) The MIT Press
    • G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," in Advances in Neural Information Processing Systems (T. K. Leen, T. G. Dietterich, and V. Tresp, eds.), vol. 13, (Cambridge, Massachussetts), pp. 409-415, The MIT Press, 2001.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 409-415
    • Cauwenberghs, G.1    Poggio, T.2
  • 2
    • 0141556297 scopus 로고    scopus 로고
    • On-line support vector machines for function approximation
    • University Politecnica de Catalunya
    • M. Martin, "On-line support vector machines for function approximation," tech. rep., Software Department, University Politecnica de Catalunya, 2002.
    • (2002) Tech. Rep., Software Department
    • Martin, M.1
  • 3
    • 0141765796 scopus 로고    scopus 로고
    • Accurate online support vector regression
    • J. Ma and J. Theiler, "Accurate online support vector regression," Neural Computation, vol. 15, no. 11, pp. 2683-2703, 2003.
    • (2003) Neural Computation , vol.15 , Issue.11 , pp. 2683-2703
    • Ma, J.1    Theiler, J.2
  • 4
    • 33745777639 scopus 로고    scopus 로고
    • Incremental support vector learning: Analysis, implementation and applications
    • P. Laskov, C. Gehl, S. Kruger, and K.-R. Muller, "Incremental support vector learning: Analysis, implementation and applications," Journal of Machine Learning Research, vol. 7, pp. 1909-1936, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1909-1936
    • Laskov, P.1    Gehl, C.2    Kruger, S.3    Muller, K.-R.4
  • 6
    • 34249726632 scopus 로고    scopus 로고
    • Efficient computation and model selection for the support vector regression
    • L. Gunter and J. Zhu, "Efficient computation and model selection for the support vector regression," Neural Computation, vol. 19, no. 6, pp. 1633-1655, 2007.
    • (2007) Neural Computation , vol.19 , Issue.6 , pp. 1633-1655
    • Gunter, L.1    Zhu, J.2
  • 7
    • 54349106864 scopus 로고    scopus 로고
    • A new solution path algorithm in support vector regression
    • G. Wang, D.-Y. Yeung, and F. H. Lochovsky, "A new solution path algorithm in support vector regression," IEEE Transactions on Neural Networks, vol. 19, no. 10, pp. 1753-1767, 2008.
    • (2008) IEEE Transactions on Neural Networks , vol.19 , Issue.10 , pp. 1753-1767
    • Wang, G.1    Yeung, D.-Y.2    Lochovsky, F.H.3
  • 12
    • 2542639357 scopus 로고    scopus 로고
    • An efficient method for computing leave-one-out error in support vector machines
    • M. M. Lee, S. S. Keerthi, C. J. Ong, and D. DeCoste, "An efficient method for computing leave-one-out error in support vector machines," IEEE transaction on neural networks, vol. 15, no. 3, pp. 750-757, 2004.
    • (2004) IEEE Transaction on Neural Networks , vol.15 , Issue.3 , pp. 750-757
    • Lee, M.M.1    Keerthi, S.S.2    Ong, C.J.3    DeCoste, D.4
  • 13
  • 14
    • 42249094907 scopus 로고    scopus 로고
    • Support vector machine solvers
    • (L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, eds.) Cambridge, MA.: MIT Press
    • L. Bottou and C.-J. Lin, "Support vector machine solvers," in Large Scale Kernel Machines (L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, eds.), pp. 301-320, Cambridge, MA.: MIT Press, 2007.
    • (2007) Large Scale Kernel Machines , pp. 301-320
    • Bottou, L.1    Lin, C.-J.2
  • 15
    • 70450206749 scopus 로고    scopus 로고
    • Efficient leave-m-out cross-validation of support vector regression by generalizing decremental algorithm
    • Special Issue on Data-Mining and Statistical Science
    • M. Karasuyama, I. Takeuchi, and R. Nakano, "Efficient leave-m-out cross-validation of support vector regression by generalizing decremental algorithm," New Generation Computing, vol. 27, no. 4, Special Issue on Data-Mining and Statistical Science, pp. 307-318, 2009.
    • (2009) New Generation Computing , vol.27 , Issue.4 , pp. 307-318
    • Karasuyama, M.1    Takeuchi, I.2    Nakano, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.