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Volumn 37, Issue 5, 2017, Pages 28-39

Analytic: An active learning system for trajectory classification

Author keywords

active learning; ANALYTiC platform; computer graphics; geographic data science; semantic annotation; trajectory classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER GRAPHICS; LEARNING SYSTEMS; SEMANTICS; TRAJECTORIES;

EID: 85030114581     PISSN: 02721716     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCG.2017.3621221     Document Type: Article
Times cited : (47)

References (8)
  • 2
    • 84881223621 scopus 로고    scopus 로고
    • Semantic trajectories modeling, and analysis
    • article 42
    • C. Parent, et al., "Semantic Trajectories Modeling, and Analysis," ACM Computing Surveys, vol. 45, no. 4, 2013, article 42
    • (2013) ACM Computing Surveys , vol.45 , Issue.4
    • Parent, C.1
  • 4
    • 85012865975 scopus 로고    scopus 로고
    • Active learning: An empirical study of common baselines
    • M. E. Ramirez-Loaiza, et al., "Active Learning: An Empirical Study of Common Baselines," Data Mining, and Knowledge Discovery, vol. 31, no. 2, 2017, pp. 287-313
    • (2017) Data Mining, and Knowledge Discovery , vol.31 , Issue.2 , pp. 287-313
    • Ramirez-Loaiza, M.E.1
  • 8
    • 80555140075 scopus 로고    scopus 로고
    • Scikit-learn: Machine learning in python
    • Oct
    • F. Pedregosa, et al., "Scikit-Learn: Machine Learning in Python," J. Machine Learning Research, vol. 12, Oct. 2011, pp. 2825-2830
    • (2011) J. Machine Learning Research , vol.12 , pp. 2825-2830
    • Pedregosa, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.