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Volumn 1, Issue , 2011, Pages 890-895

Partially supervised text classification with multi-level examples

Author keywords

[No Author keywords available]

Indexed keywords

EXAMPLE-BASED LEARNING METHOD; HIGH QUALITY; MULTI-LEVEL; MULTIPLE LEVELS; TEXT CLASSIFICATION; TEXT CLASSIFIERS; TRAINING DATA; WEIGHTED SUPPORT VECTOR MACHINE;

EID: 80055061482     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (5)

References (19)
  • 1
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • PII S0004370297000635
    • Blum A. L. and Langley P. 1997. Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence 97(1):245-271. (Pubitemid 127401106)
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 2
    • 27744465529 scopus 로고    scopus 로고
    • Learning from positive and unlabeled examples
    • DOI 10.1016/j.tcs.2005.09.007, PII S0304397505005256, Algorithmic Learning Theory (ALT 2000)
    • Denis F., Gilleron R., and Letouzey F. 2005. Learning from Positive and Unlabeled Examples. Theoretical Computer Science 348(1):70-83. (Pubitemid 41625712)
    • (2005) Theoretical Computer Science , vol.348 , Issue.1 , pp. 70-83
    • Denis, F.1    Gilleron, R.2    Letouzey, F.3
  • 5
    • 84957069814 scopus 로고    scopus 로고
    • Text Categorization with Support Vector Machines: Learning with Many Relevant Features
    • Chemnitz, Germany
    • Joachims T. 1998. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the 10th European Conference on Machine Learning, 137-142. Chemnitz, Germany.
    • (1998) Proceedings of the 10th European Conference on Machine Learning , pp. 137-142
    • Joachims, T.1
  • 6
    • 1942516926 scopus 로고    scopus 로고
    • Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
    • Washington DC, United States
    • Lee W. S. and Liu B. 2003. Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression. In Proceedings of the 20th International Conference on Machine Learning, 448-455. Washington DC, United States.
    • (2003) Proceedings of the 20th International Conference on Machine Learning , pp. 448-455
    • Lee, W.S.1    Liu, B.2
  • 13
    • 22944440839 scopus 로고    scopus 로고
    • Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
    • Navigli R. and Velardi P. 2004. Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites. Computational Linguistics 30(2):151-179.
    • (2004) Computational Linguistics , vol.30 , Issue.2 , pp. 151-179
    • Navigli, R.1    Velardi, P.2
  • 15
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using EM
    • Nigam K., McCallum A. K., Thrun S., and Mitchell T. 2000. Text Classification from Labeled and Unlabeled Documents Using EM. Machine Learning 39(2/3):103-134. (Pubitemid 30594822)
    • (2000) Machine Learning , vol.39 , Issue.2 , pp. 103-134
    • Nigam, K.1    Mccallum, A.K.2    Thrun, S.3    Mitchell, T.4
  • 16
    • 0002442796 scopus 로고    scopus 로고
    • Machine Learning in Automated Text Categorization
    • Sebastiani F. 2002. Machine Learning in Automated Text Categorization. ACM Computer Surveys 34(1):1-47.
    • (2002) ACM Computer Surveys , vol.34 , Issue.1 , pp. 1-47
    • Sebastiani, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.