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Volumn 35, Issue , 2006, Pages 61-84

Selecting data for fast support vector machines training

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EID: 33751005852     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-36122-0_3     Document Type: Article
Times cited : (30)

References (23)
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    • Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C. J. C., Smola, A. J. (eds.): Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA (1999) 169-184
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    • Reducing the number of training samples for fast support vector machine classification
    • Koggalage, R., Halgamuge, S.: Reducing the number of training samples for fast support vector machine classification. Neural Information Processing - Letters and Reviews 2(3) (2004) 57-65
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    • Koggalage, R.1    Halgamuge, S.2
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    • Reduced support vector machines: A statistical theory
    • Technical report, Institute of Statistical Science, Academia Sinica, Taiwan
    • Huang, S. Y., Lee, Y. J.: RAduced support vector machines: a statistical theory. Technical report, Institute of Statistical Science, Academia Sinica, Taiwan. http://www.stat.sinica.edu.tw/syhuang/ (2004)
    • (2004)
    • Huang, S.Y.1    Lee, Y.J.2
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    • Functions of positive and negative type and their connection with the theory of integral equations
    • Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Roy. Soc. London, A 209 (1909) 415-446
    • (1909) Philos. Trans. Roy. Soc. London, A , vol.209 , pp. 415-446
    • Mercer, J.1
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    • Theoretical foundations of the potential function method in pattern recognition learning
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    • Aizerman, M.1    Braverman, E.2    Rozonoer, L.3
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    • Support vector machines: Training and applications
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    • Osuna, E., Freund, T., Girosi, F.: Support vector machines: training and applications. A.I. Memo AIM - 1602, MIT A.I. Lab. (1996)
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    • Osuna, E.1    Freund, R.2    Girosi, F.3
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    • Fast training of support vector machines using sequential minimal optimization
    • In: Schölkopf, B., Burges, C. J. C., Smola, A. J. (eds.): MIT Press, Cambridge, MA
    • Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C. J. C., Smola, A. J. (eds.): Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA (1999) 185-208
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    • Neighborhood selection in the k-nearest neighbor rule using statistical confidence
    • Wang, J, Neskovic, P, Cooper, L. N: Neighborhood selection in the k-nearest neighbor rule using statistical confidence. Pattern Recognition. Vol. 39 (3) (2006) 417-423
    • (2006) Pattern Recognition , vol.39 , Issue.3 , pp. 417-423
    • Wang, J.1    Neskovic, P.2    Cooper, L.N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.