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Volumn 36, Issue 3 PART 2, 2009, Pages 6826-6832

Feature selection with a measure of deviations from Poisson in text categorization

Author keywords

Feature selection; k NN classifier; Poisson distribution; Support vector machine; Text categorization

Indexed keywords

CLASSIFIERS; FEATURE EXTRACTION; NEAREST NEIGHBOR SEARCH; POISSON DISTRIBUTION; SUPPORT VECTOR MACHINES; TEXT PROCESSING;

EID: 58349094495     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.08.006     Document Type: Article
Times cited : (77)

References (15)
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    • Church, K. W., & Gale, W. A. (1995). Inverse document frequency (IDF): A measure of deviation from Poisson. In Proceedings of the third workshop on very large corpora (pp. 121-130).
  • 3
    • 84880904111 scopus 로고    scopus 로고
    • Feature selection for text classification
    • Chapman and Hall/CRC Press, USA
    • Forman G. Feature selection for text classification. Computational methods of feature selection (2007), Chapman and Hall/CRC Press, USA 57-276
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    • Forman, G.1
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    • 58349104692 scopus 로고    scopus 로고
    • Jansche, M. (2003). Parametric models of linguistic count data. In Proceedings of the 41st annual meeting on association for computational linguistics (Vol. 1, pp. 288-295).
    • Jansche, M. (2003). Parametric models of linguistic count data. In Proceedings of the 41st annual meeting on association for computational linguistics (Vol. 1, pp. 288-295).
  • 6
    • 84957069814 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • Springer, New York
    • Joachims T. Text categorization with support vector machines: Learning with many relevant features. Proceedings of the 10th European conference on machine learning (1998), Springer, New York 137-142
    • (1998) Proceedings of the 10th European conference on machine learning , pp. 137-142
    • Joachims, T.1
  • 7
    • 58349098025 scopus 로고    scopus 로고
    • Joachims, T. (2002). Learning to classify text using support vector machines. Dissertation. Kluwer.
    • Joachims, T. (2002). Learning to classify text using support vector machines. Dissertation. Kluwer.
  • 9
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    • Lewis, D. D. (1997). Reuters-21578 text categorization collection. .
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    • Machine learning in automated text categorization
    • Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 34 1 (2002) 1-47
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    • Sebastiani, F.1
  • 14
    • 0038632285 scopus 로고    scopus 로고
    • Using suffix arrays to compute term frequency and document frequency for all substrings in a corpus
    • Yamamoto M., and Church K.W. Using suffix arrays to compute term frequency and document frequency for all substrings in a corpus. Computational Linguistics 27 1 (2001) 1-30
    • (2001) Computational Linguistics , vol.27 , Issue.1 , pp. 1-30
    • Yamamoto, M.1    Church, K.W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.