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Volumn , Issue , 2008, Pages 916-920

Using ambiguity measure feature selection algorithm for support vector machine classifier

Author keywords

Feature selection; SVM; Text classification

Indexed keywords

AMPLITUDE MODULATION; CLASSIFIERS; COMPUTATIONAL METHODS; DIGITAL LIBRARIES; IMAGE RETRIEVAL; LEARNING SYSTEMS; LIBRARIES; SEMANTICS; SUPPORT VECTOR MACHINES; VECTORS;

EID: 56749165619     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1363686.1363896     Document Type: Conference Paper
Times cited : (8)

References (17)
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    • Cortes, C.1    Vapnik, V.2
  • 4
    • 0002714543 scopus 로고    scopus 로고
    • Making Large-scale support vector machine learning practical
    • B. Schölkopf et al, Eds, MIT Press
    • Joachims T., Making Large-scale support vector machine learning practical. In B. Schölkopf et al. (Eds.), Advances in kernel methods: Support vector learning. MIT Press, 1999, pg. 169-184.
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    • Joachims, T.1
  • 5
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    • Text Categorization with Support Vector Machines: Learning with many relevant features
    • Joachims T., Text Categorization with Support Vector Machines: Learning with many relevant features. 10th European Conference on Machine Learning, 1998, pg. 137-142.
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    • Joachims, T.1
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    • Newsgroups Dataset
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    • Original , vol.20
    • Lang, K.1
  • 8
    • 15544362881 scopus 로고    scopus 로고
    • Lewis D., Reuters-21578, http://www.daviddlewis.com/resources/ testcollections/reuters21578.
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  • 17
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    • Zheng Z., Srihari R., Optimally Combining Positive and Negative Features for Text Categorization. In Proceedings of the ICML, Workshop on Learning from Imbalanced Datasets II, Washington DC, 2003.
    • Zheng Z., Srihari R., Optimally Combining Positive and Negative Features for Text Categorization. In Proceedings of the ICML, Workshop on Learning from Imbalanced Datasets II, Washington DC, 2003.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.