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Volumn 7, Issue , 2006, Pages 1437-1466

Maximum-gain working set selection for SVMs

Author keywords

Large scale optimization; Quadratic programming; Sequential minimal optimization; Support vector machines; Working set selection

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; ITERATIVE METHODS; OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

EID: 33745784639     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (76)

References (20)
  • 5
    • 29144499905 scopus 로고    scopus 로고
    • Working set selection using the second order information for training support vector machines
    • R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training support vector machines. Journal of Machine Learning Research, 6:1889-1918, 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 1889-1918
    • Fan, R.-E.1    Chen, P.-H.2    Lin, C.-J.3
  • 6
    • 0037399781 scopus 로고    scopus 로고
    • Polynomial-time decomposition algorithms for support vector machines
    • D. Hush and C. Scovel. Polynomial-time decomposition algorithms for support vector machines. Machine Learning, 51:51-71, 2003.
    • (2003) Machine Learning , vol.51 , pp. 51-71
    • Hush, D.1    Scovel, C.2
  • 7
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • B. Schölkopf, C. Burges, and A. Smola, editors, chapter 11. MIT Press
    • T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, chapter 11, pages 169-184. MIT Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 8
    • 0036163654 scopus 로고    scopus 로고
    • Convergence of a generalized SMO algorithm for SVM classifier design
    • S. S. Keerthi and E. G. Gilbert. Convergence of a generalized SMO algorithm for SVM classifier design. Machine Learning, 46:351-360, 2002.
    • (2002) Machine Learning , vol.46 , pp. 351-360
    • Keerthi, S.S.1    Gilbert, E.G.2
  • 10
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 11
    • 0035506741 scopus 로고    scopus 로고
    • On the convergence of the decomposition method for support vector machines
    • C.-J. Lin. On the convergence of the decomposition method for support vector machines. IEEE Transactions on Neural Networks, 12:1288-1298, 2001.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , pp. 1288-1298
    • Lin, C.-J.1
  • 12
    • 9444296042 scopus 로고    scopus 로고
    • A general convergence theorem for the decomposition method
    • John Shawe-Taylor and Yoram Singer, editors, Proceedings of the 17th Annual Conference on Learning Theory, COLT 2004. Springer-Verlag
    • N. List and H. U. Simon. A general convergence theorem for the decomposition method. In John Shawe-Taylor and Yoram Singer, editors, Proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, volume 3120 of LNCS, pages 363-377. Springer-Verlag, 2004.
    • (2004) LNCS , vol.3120 , pp. 363-377
    • List, N.1    Simon, H.U.2
  • 13
    • 26944489027 scopus 로고    scopus 로고
    • General polynomial time decomposition algorithms
    • Peter Auer and Ron Meir, editors, Proceedings of the 18th Annual Conference on Learning Theory, COLT 2005. Springer-Verlag
    • N. List and H. U. Simon. General polynomial time decomposition algorithms. In Peter Auer and Ron Meir, editors, Proceedings of the 18th Annual Conference on Learning Theory, COLT 2005, volume 3559 of LNCS, pages 308-322. Springer-Verlag, 2005.
    • (2005) LNCS , vol.3559 , pp. 308-322
    • List, N.1    Simon, H.U.2
  • 14
    • 0031334889 scopus 로고    scopus 로고
    • Improved training algorithm for support vector machines
    • J. Principe, L. Giles, N. Morgan, and E. Wilson, editors. IEEE Press
    • E. Osuna, R. Freund, and F. Girosi. Improved training algorithm for support vector machines. In J. Principe, L. Giles, N. Morgan, and E. Wilson, editors, Neural Networks for Signal Processing VII, pages 276-285. IEEE Press, 1997.
    • (1997) Neural Networks for Signal Processing VII , pp. 276-285
    • Osuna, E.1    Freund, R.2    Girosi, F.3
  • 15
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, chapter 12. MIT Press
    • J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, chapter 12, pages 185-208. MIT Press, 1999.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.1
  • 17
    • 19344375172 scopus 로고    scopus 로고
    • Rigorous proof of termination of SMO algorithm for support vector machines
    • N. Takahashi and T. Nishi. Rigorous proof of termination of SMO algorithm for support vector machines. IEEE Transaction on Neural Networks, 16(3):774-776, 2005.
    • (2005) IEEE Transaction on Neural Networks , vol.16 , Issue.3 , pp. 774-776
    • Takahashi, N.1    Nishi, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.