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Volumn 7, Issue , 2006, Pages 1909-1936

Incremental support vector learning: Analysis, implementation and applications

Author keywords

Drug discovery; Incremental SVM; Intrusion detection; Online learning

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; CONDITION MONITORING; CONVERGENCE OF NUMERICAL METHODS; ONLINE SYSTEMS;

EID: 33745777639     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (348)

References (38)
  • 1
    • 0000710299 scopus 로고
    • Queries and concept learning
    • D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.
    • (1988) Machine Learning , vol.2 , pp. 319-342
    • Angluin, D.1
  • 5
    • 80052866161 scopus 로고    scopus 로고
    • Incremental and decremental support vector machine learning
    • T. K. Leen, T. G. Dietterich, and V. Tresp, editors. MIT Press
    • G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13, pages 409-415. MIT Press, 2001.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 409-415
    • Cauwenberghs, G.1    Poggio, T.2
  • 7
    • 0004614981 scopus 로고    scopus 로고
    • Libsvm: Introduction and benchmarks
    • Department of Computer Science and Information Engineering, National Taiwan University, Taipei
    • C.-C. Chang and C.-J. Lin. Libsvm: Introduction and benchmarks. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2000.
    • (2000) Technical Report
    • Chang, C.-C.1    Lin, C.-J.2
  • 8
    • 0000913324 scopus 로고    scopus 로고
    • SVM torch: Support vector machines for large-scale regression problems
    • R. Collobert and S. Bengio. SVM Torch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160, 2001.
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 9
    • 84897965802 scopus 로고    scopus 로고
    • AUC optimization vs. error rate minimization
    • C. Cortes and M. Mohri. AUC optimization vs. error rate minimization. In Proc. NIPS'2003, 2004.
    • (2004) Proc. NIPS'2003
    • Cortes, C.1    Mohri, M.2
  • 11
    • 0004236492 scopus 로고    scopus 로고
    • John Hopkins University Press, Baltimore, London, 3rd edition
    • G. H. Golub and C. F. van Loan. Matrix Computations. John Hopkins University Press, Baltimore, London, 3rd edition, 1996.
    • (1996) Matrix Computations
    • Golub, G.H.1    Van Loan, C.F.2
  • 12
    • 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, pages 169-184, Cambridge, MA, 1999. MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 14
    • 0001849163 scopus 로고
    • How good is the simplex algorithm?
    • O. Sisha, editor, Academic Press
    • F. Klee and G. J. Minty. How good is the simplex algorithm? In O. Sisha, editor, Inequalities III, pages 159-175. Academic Press, 1972.
    • (1972) Inequalities III , pp. 159-175
    • Klee, F.1    Minty, G.J.2
  • 15
    • 0036158636 scopus 로고    scopus 로고
    • Feasible direction decomposition algorithms for training support vector machines
    • P. Laskov. Feasible direction decomposition algorithms for training support vector machines. Machine Learning, 46:315-349, 2002.
    • (2002) Machine Learning , vol.46 , pp. 315-349
    • Laskov, P.1
  • 16
    • 85016684916 scopus 로고    scopus 로고
    • Intrusion detection in unlabeled data with quarter-sphere support vector machines
    • P. Laskov, C. Schäfer, and I. Kotenko. Intrusion detection in unlabeled data with quarter-sphere support vector machines. In Proc. DIMVA, pages 71-82, 2004.
    • (2004) Proc. DIMVA , pp. 71-82
    • Laskov, P.1    Schäfer, C.2    Kotenko, I.3
  • 17
    • 0001857994 scopus 로고    scopus 로고
    • Efficient backprop
    • G. Orr and K.-R. Müller, editors, Heidelberg, New York. Springer LNCS
    • Y. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller. Efficient backprop. In G. Orr and K.-R. Müller, editors, Neural Networks: Tricks of the Trade, volume 1524, pages 9-53, Heidelberg, New York, 1998. Springer LNCS.
    • (1998) Neural Networks: Tricks of the Trade , vol.1524 , pp. 9-53
    • LeCun, Y.1    Bottou, L.2    Orr, G.B.3    Müller, K.-R.4
  • 18
    • 30244571334 scopus 로고
    • On-line learning of linear functions
    • University of California at Santa Cruz, October
    • N. Littlestone, P. M. Long, and M. K. Warmuth. On-line learning of linear functions. Technical Report CRL-91-29, University of California at Santa Cruz, October 1991.
    • (1991) Technical Report , vol.CRL-91-29
    • Littlestone, N.1    Long, P.M.2    Warmuth, M.K.3
  • 19
    • 0141682928 scopus 로고    scopus 로고
    • Time-series novelty detection using one-class Support Vector Machines
    • to appear
    • J. Ma and S. Perkins. Time-series novelty detection using one-class Support Vector Machines. In IJCNN, 2003. to appear.
    • (2003) IJCNN
    • Ma, J.1    Perkins, S.2
  • 21
    • 0141556297 scopus 로고    scopus 로고
    • On-line Support Vector Machines for function approximation
    • Universitat Politècnica de Catalunya, Departement de Llengatges i Sistemes Informàtics
    • M. Martin. On-line Support Vector Machines for function approximation. Technical report, Universitat Politècnica de Catalunya, Departement de Llengatges i Sistemes Informàtics, 2002.
    • (2002) Technical Report
    • Martin, M.1
  • 23
    • 0036592037 scopus 로고    scopus 로고
    • On-line learning in changing environments with applications in supervised and unsupervised learning
    • N. Murata, M. Kawanabe, A. Ziehe, K.-R. Müller, and S.-I. Amari. On-line learning in changing environments with applications in supervised and unsupervised learning. Neural Networks, 15 (4-6):743-760, 2002.
    • (2002) Neural Networks , vol.15 , Issue.4-6 , pp. 743-760
    • Murata, N.1    Kawanabe, M.2    Ziehe, A.3    Müller, K.-R.4    Amari, S.-I.5
  • 24
    • 84898987101 scopus 로고    scopus 로고
    • Adaptive on-line learning in changing environments
    • M. C. Mozer, M. I. Jordan, and T. Petsche, editors. The MIT Press
    • N. Murata, K.-R. Müller, A. Ziehe, and S. i. Amari. Adaptive on-line learning in changing environments. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems, volume 9, page 599. The MIT Press, 1997.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 599
    • Murata, N.1    Müller, K.-R.2    Ziehe, A.3    Amari, S.I.4
  • 25
    • 0004135065 scopus 로고    scopus 로고
    • G. Orr and K.-R. Müller, editors. Springer LNCS
    • G. Orr and K.-R. Müller, editors. Neural Networks: Tricks of the Trade, volume 1524. Springer LNCS, 1998.
    • (1998) Neural Networks: Tricks of the Trade , vol.1524
  • 26
    • 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. 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, pages 185-208, Cambridge, MA, 1999. MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.1
  • 27
    • 84958962423 scopus 로고    scopus 로고
    • Incremental support vector machine learning: A local approach
    • L. Ralaivola and F. d'Alché Buc. Incremental support vector machine learning: A local approach. Lecture Notes in Computer Science, 2130:322-329, 2001.
    • (2001) Lecture Notes in Computer Science , vol.2130 , pp. 322-329
    • Ralaivola, L.1    D'Alché Buc, F.2
  • 28
    • 0000016172 scopus 로고
    • A stochastic approximation method
    • H. Robbins and S. Munro. A stochastic approximation method. Ann. Math. Stat., 22:400-407, 1951.
    • (1951) Ann. Math. Stat. , vol.22 , pp. 400-407
    • Robbins, H.1    Munro, S.2
  • 29
    • 33646351867 scopus 로고    scopus 로고
    • Incremental learning with support vector machines
    • Universität Dortmund, SFB475
    • S. Rüping. Incremental learning with support vector machines. Technical Report TR-18, Universität Dortmund, SFB475, 2002.
    • (2002) Technical Report , vol.TR-18
    • Rüping, S.1
  • 30
    • 0004069068 scopus 로고    scopus 로고
    • D. Saad, editor. Cambridge University Press
    • D. Saad, editor. On-line learning in neural networks. Cambridge University Press, 1998.
    • (1998) On-line Learning in Neural Networks
  • 33
    • 4344619819 scopus 로고    scopus 로고
    • Incremental learning with support vector machines
    • N. A. Syed, H. Liu, and K. K. Sung. Incremental learning with support vector machines. In SVM workshop, IJCAI, 1999.
    • (1999) SVM Workshop, IJCAI
    • Syed, N.A.1    Liu, H.2    Sung, K.K.3
  • 34
    • 0001986205 scopus 로고    scopus 로고
    • Data domain description by support vectors
    • M. Verleysen, editor Brussels,. D. Facto Press
    • D. Tax and R. Duin. Data domain description by support vectors. In M. Verleysen, editor, Proc. ESANN, pages 251-256, Brussels, 1999. D. Facto Press.
    • (1999) Proc. ESANN , pp. 251-256
    • Tax, D.1    Duin, R.2
  • 35
    • 79960753941 scopus 로고    scopus 로고
    • Online SVM learning: From classification to data description and back
    • C. et al. Molina, editor
    • D. M. J. Tax and P. Laskov. Online SVM learning: from classification to data description and back. In C. et al. Molina, editor, Proc. NNSP, pages 499-508, 2003.
    • (2003) Proc. NNSP , pp. 499-508
    • Tax, D.M.J.1    Laskov, P.2
  • 36


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.