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Volumn 20, Issue 2, 2008, Pages 374-382

Second-order SMO improves SVM online and active learning

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; HUMAN; LEARNING; MATHEMATICAL COMPUTING; ONLINE SYSTEM;

EID: 38649118902     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2007.10-06-354     Document Type: Article
Times cited : (23)

References (10)
  • 1
    • 25444522689 scopus 로고    scopus 로고
    • Fast kernel classifiers with on-line and active learning
    • Available online at
    • Bordes, A., Ertekin, S., Weston, J., & Bottou, L. (2005). Fast kernel classifiers with on-line and active learning. Journal of Machine Learning Research, 5, 1579-1619. Available online at http://leon.bottou.com/projects/ lasvm.
    • (2005) Journal of Machine Learning Research , vol.5 , pp. 1579-1619
    • Bordes, A.1    Ertekin, S.2    Weston, J.3    Bottou, L.4
  • 2
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 3
    • 29144499905 scopus 로고    scopus 로고
    • Working set selection using the second order information for training support vector machines
    • Fan, R.-E., Chen, P-H., & Lin, C. J. (2005). Working set selection using the second order information for training support vector machines. Journal of Machine Learning Research, 6, 1889-1918.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 1889-1918
    • Fan, R.-E.1    Chen, P.-H.2    Lin, C.J.3
  • 4
    • 23944487822 scopus 로고    scopus 로고
    • Gradient-based adaptation of general gaussian kernels
    • Glasmachers, T., & Igel, C. (2005). Gradient-based adaptation of general gaussian kernels. Neural Computation, 17, 2099-2105.
    • (2005) Neural Computation , vol.17 , pp. 2099-2105
    • Glasmachers, T.1    Igel, C.2
  • 5
    • 33745784639 scopus 로고    scopus 로고
    • Maximum-gain working set selection for support vector machines
    • Glasmachers, T., & Igel, C. (2006). Maximum-gain working set selection for support vector machines. Journal of Machine Learning Research, 7, 1437-1466.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1437-1466
    • Glasmachers, T.1    Igel, C.2
  • 6
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • B. Schölkopf, C. Burges, & A. Smola Eds, Cambridge, MA: MIT Press
    • Joachims, T. (1999). Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods-Support vector learning (pp. 169-184). Cambridge, MA: MIT Press.
    • (1999) Advances in kernel methods-Support vector learning , pp. 169-184
    • Joachims, T.1
  • 8
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Schölkopf, C. J. C. Burges, & A. J. Smola Eds, Cambridge, MA: MIT Press
    • Piatt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in kernel methods-Support vector learning (pp. 185-208). Cambridge, MA: MIT Press.
    • (1999) Advances in kernel methods-Support vector learning , pp. 185-208
    • Piatt, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.