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Volumn 2006, Issue , 2006, Pages 3682-3686

An efficient SVM-based spam filtering algorithm

Author keywords

Feature selection; Spam filtering; SVM; Text classification

Indexed keywords

ALGORITHMS; ELECTRONIC MAIL; FEATURE EXTRACTION; HYPERTEXT SYSTEMS; PROBLEM SOLVING; SPAMMING; SUPPORT VECTOR MACHINES;

EID: 33947202277     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2006.258626     Document Type: Conference Paper
Times cited : (6)

References (18)
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  • 7
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  • 10
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    • Text categorization with support vector machines: Learning with many relevant features
    • Chemnitz,pp, April
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  • 14
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    • Sequential minimal optimization: A fast algorithm for training support vector machines
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  • 15
    • 0031272926 scopus 로고    scopus 로고
    • B.Schoelkopf, K. Sung.C.Burges,F.Girosi, P.Niyogi, T. Poggio, and V.Vapnik, Comparing support vector machines with Gaussian kernels to radial basis function classifiers, IEEE Transactions on Signal Processing, 45, No.11,pp.2758-2765, November 1997.
    • B.Schoelkopf, K. Sung.C.Burges,F.Girosi, P.Niyogi, T. Poggio, and V.Vapnik, Comparing support vector machines with Gaussian kernels to radial basis function classifiers, IEEE Transactions on Signal Processing, Vol.45, No.11,pp.2758-2765, November 1997.
  • 16
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    • Bounds on error expectation for support vector machines
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    • V.Vapnik, and O.Chapelle, Bounds on error expectation for support vector machines, Neural Computation, Vol.12,No.9, pp.2013-2036, September 2000.
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  • 17
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    • Choosing multiple parameters for support vector machines
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    • O.Chapelle, V.Vapnik, O.Bousquet and S.Mukherjee, Choosing multiple parameters for support vector machines, Machine Learning, Vol.46, No.1, pp. 131-159, January 2002.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.