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Volumn , Issue , 2013, Pages 1685-1690

Discovering health-related knowledge in social media using ensembles of heterogeneous features

Author keywords

Classification; Machine learning; Social media

Indexed keywords

DOCUMENT CLASSIFICATION; HETEROGENEOUS FEATURES; MACHINE LEARNING TECHNIQUES; N-GRAMS; SOCIAL MEDIA; SOCIAL MEDIA DATUM; SURVEILLANCE SYSTEMS;

EID: 84889601386     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2505515.2505629     Document Type: Conference Paper
Times cited : (26)

References (28)
  • 5
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 8
    • 84889566005 scopus 로고    scopus 로고
    • Syndromic classification of twitter messages
    • abs/1110.3094
    • N. Collier and S. Doan. Syndromic classification of twitter messages. CoRR, abs/1110.3094, 2011.
    • (2011) CoRR
    • Collier, N.1    Doan, S.2
  • 13
    • 84989296687 scopus 로고    scopus 로고
    • Utilizing context in generative Bayesian models for linked corpus
    • S. Kataria, P. Mitra, and S. Bhatia. Utilizing context in generative bayesian models for linked corpus. In AAAI, 2010.
    • (2010) AAAI
    • Kataria, S.1    Mitra, P.2    Bhatia, S.3
  • 19
    • 84865675933 scopus 로고    scopus 로고
    • A model for mining public health topics from twitter
    • M. J. Paul and M. Dredze. A model for mining public health topics from twitter. Technical report, 2011.
    • (2011) Technical Report
    • Paul, M.J.1    Dredze, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.