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Volumn 3734 LNAI, Issue , 2005, Pages 45-62

Training support vector machines via SMO-type decomposition methods

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC STABILITY; CONVERGENCE OF NUMERICAL METHODS; OPTIMIZATION; PERSONNEL TRAINING; THEOREM PROVING;

EID: 33646521266     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564089_6     Document Type: Conference Paper
Times cited : (6)

References (19)
  • 2
    • 0034228643 scopus 로고    scopus 로고
    • The analysis of decomposition methods for support vector machines
    • C.-C. Chang, C.-W. Hsu, and C.-J. Lin. The analysis of decomposition methods for support vector machines. IEEE Transactions on Neural Networks, 11(4):1003-1008, 2000.
    • (2000) IEEE Transactions on Neural Networks , vol.11 , Issue.4 , pp. 1003-1008
    • Chang, C.-C.1    Hsu, C.-W.2    Lin, C.-J.3
  • 4
    • 26944441889 scopus 로고    scopus 로고
    • A study on SMO-type decomposition methods for support vector machines
    • Department of Computer Science, National Taiwan University
    • P.-H. Chen, R.-E. Fan, and C.-J. Lin. A study on SMO-type decomposition methods for support vector machines. Technical report, Department of Computer Science, National Taiwan University, 2005. http://www.csie.ntu.edu.tw/~cjlin/ papers/generalSMO.pdf.
    • (2005) Technical Report
    • Chen, P.-H.1    Fan, R.-E.2    Lin, C.-J.3
  • 5
    • 34249753618 scopus 로고
    • Support-vector network
    • C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273-297, 1995.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 6
    • 26944501776 scopus 로고    scopus 로고
    • Working set selection using the second order information for training SVM
    • Department of Computer Science, National Taiwan University
    • R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM. Technical report, Department of Computer Science, National Taiwan University, 2005.
    • (2005) Technical Report
    • Fan, R.-E.1    Chen, P.-H.2    Lin, C.-J.3
  • 7
    • 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
  • 8
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Cambridge, MA, MIT Press
    • T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.
    • (1998) Advances in Kernel Methods - Support Vector Learning
    • Joachims, T.1
  • 9
    • 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
    • 0013376452 scopus 로고    scopus 로고
    • On the role of the threshold parameter in SVM training algorithms
    • Department of Mechanical and Production Engineering, National University of Singapore, Singapore
    • S. S. Keerthi and C. J. Ong. On the role of the threshold parameter in SVM training algorithms. Technical Report CD-00-09, Department of Mechanical and Production Engineering, National University of Singapore, Singapore, 2000.
    • (2000) Technical Report CD-00-09
    • Keerthi, S.S.1    Ong, C.J.2
  • 12
    • 0038178786 scopus 로고    scopus 로고
    • Linear convergence of a decomposition method for support vector machines
    • Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
    • C.-J. Lin. Linear convergence of a decomposition method for support vector machines. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2001.
    • (2001) Technical Report
    • Lin, C.-J.1
  • 13
    • 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(6):1288-1298, 2001.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.6 , pp. 1288-1298
    • Lin, C.-J.1
  • 14
    • 0036129250 scopus 로고    scopus 로고
    • Asymptotic convergence of an SMO algorithm without any assumptions
    • C.-J. Lin. Asymptotic convergence of an SMO algorithm without any assumptions. IEEE Transactions on Neural Networks, 13(1):248-250, 2002.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.1 , pp. 248-250
    • Lin, C.-J.1
  • 15
    • 0036737295 scopus 로고    scopus 로고
    • A formal analysis of stopping criteria of decomposition methods for support vector machines
    • C.-J. Lin. A formal analysis of stopping criteria of decomposition methods for support vector machines. IEEE Transactions on Neural Networks, 13(5):1045-1052, 2002.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.5 , pp. 1045-1052
    • Lin, C.-J.1
  • 17
    • 0030673582 scopus 로고    scopus 로고
    • Training support vector machines: An application to face detection
    • New York, NY, IEEE
    • E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In Proceedings of CVPR'97, pages 130-136, New York, NY, 1997. IEEE.
    • (1997) Proceedings of CVPR'97 , pp. 130-136
    • Osuna, E.1    Freund, R.2    Girosi, F.3
  • 19
    • 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, Cambridge, MA . MIT Press
    • J. C. 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, Cambridge, MA, 1998. MIT Press.
    • (1998) Advances in Kernel Methods - Support Vector Learning
    • Platt, J.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.