메뉴 건너뛰기




Volumn 85, Issue 3-4, 2008, Pages 559-578

A machine learning methodology for the analysis of workplace accidents

Author keywords

Artificial intelligence; Data mining; Machine learning; Operations research; Statistics

Indexed keywords

ACCIDENTS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); DATA MINING; LEARNING SYSTEMS; OPERATIONS RESEARCH; QUALITY CONTROL; RISK ASSESSMENT; STATISTICS; SUPPORT VECTOR MACHINES;

EID: 41449093398     PISSN: 00207160     EISSN: 10290265     Source Type: Journal    
DOI: 10.1080/00207160701297346     Document Type: Conference Paper
Times cited : (56)

References (29)
  • 4
    • 46149134436 scopus 로고
    • Fusion, propagation and structuring in belief networks
    • Pearl, J., 1986, Fusion, propagation and structuring in belief networks. Artificial Intelligence, 29, 241-288.
    • (1986) Artificial Intelligence , vol.29 , pp. 241-288
    • Pearl, J.1
  • 7
    • 10944272650 scopus 로고    scopus 로고
    • Extreme learning machine: A new learning scheme of feedforward neural networks
    • Paper presented at the, Budapest, Hungary, pp
    • Huang, G.B., Zhu, Q. Y. and Siew CK., 2004, Extreme learning machine: a new learning scheme of feedforward neural networks. Paper presented at the International Joint Conference on Neural Networks (LJCNN2004), Budapest, Hungary, pp. 985-990.
    • (2004) International Joint Conference on Neural Networks (LJCNN2004) , pp. 985-990
    • Huang, G.B.1    Zhu, Q.Y.2    Siew, C.K.3
  • 8
    • 0000661829 scopus 로고
    • An exporatory technique for investigating large quantities of categorica; data
    • Kass, G. V., 1980, An exporatory technique for investigating large quantities of categorica; data. Applied Statistics, 29, 119-127.
    • (1980) Applied Statistics , vol.29 , pp. 119-127
    • Kass, G.V.1
  • 14
    • 33745918399 scopus 로고    scopus 로고
    • Universal approximation using incremental constructive feedforward networks with random hidden nodes
    • Huang, G.B., Chen, L. and Siew, C.K., 2006, Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17, 879-892.
    • (2006) IEEE Transactions on Neural Networks , vol.17 , pp. 879-892
    • Huang, G.B.1    Chen, L.2    Siew, C.K.3
  • 18
    • 0030124955 scopus 로고    scopus 로고
    • A guide to the literature on learning probabilistic networks from data
    • Buntime, W., 1996, A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8, 195-210.
    • (1996) IEEE Transactions on Knowledge and Data Engineering , vol.8 , pp. 195-210
    • Buntime, W.1
  • 20
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper, G.F. and Herskovits, E., 1992, A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • Cooper, G.F.1    Herskovits, E.2
  • 21
    • 0030492812 scopus 로고    scopus 로고
    • A new mediod for symbolic inference in Bayesian networks
    • Castillo, E., Gutiérrez, J.M. and Hadi, A.S., 1996, A new mediod for symbolic inference in Bayesian networks. Networks, 28, 31-43.
    • (1996) Networks , vol.28 , pp. 31-43
    • Castillo, E.1    Gutiérrez, J.M.2    Hadi, A.S.3
  • 22
    • 0001006209 scopus 로고
    • Local computations with probabilities on graphical structures and their application to expert systems
    • Lauritzen, S.L. and Spiegelhalter, DJ., 1988, Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, B 50, 157-224.
    • (1988) Journal of the Royal Statistical Society, B , vol.50 , pp. 157-224
    • Lauritzen, S.L.1    Spiegelhalter, D.J.2
  • 23
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • Heckerman, D.E., Geiger, D. and Chickering, D.M., 1995, Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20, 197-243.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.E.1    Geiger, D.2    Chickering, D.M.3
  • 24
    • 84876655728 scopus 로고    scopus 로고
    • Decision Systems Laboratory, University of Pittsburg. Available online at: http://genie.sis.pitt.edu/about.html (accessed March 2006).
    • Decision Systems Laboratory, University of Pittsburg. Available online at: http://genie.sis.pitt.edu/about.html (accessed March 2006).
  • 25
    • 0005321026 scopus 로고    scopus 로고
    • Aalborg, Denmark. Available online at:, accessed June 2006
    • Hugin Expert A/S, Aalborg, Denmark. Available online at: http://www.hugin.com/ (accessed June 2006).
    • Hugin Expert A/S
  • 26
    • 78149329086 scopus 로고    scopus 로고
    • The EQ framework for learning equivalence classes of Bayesian networks
    • Paper presented at the
    • Munteanu, P. and Bendou, M., 2001, The EQ framework for learning equivalence classes of Bayesian networks. Paper presented at the 1st IEEE International Conference on Data Mining (IEEE ICDM), pp. 417-424.
    • (2001) 1st IEEE International Conference on Data Mining (IEEE ICDM) , pp. 417-424
    • Munteanu, P.1    Bendou, M.2
  • 28
    • 84876623222 scopus 로고    scopus 로고
    • Available online at, accessed June 2006
    • Bayesia S.A., France. Available online at http://www.bayesia.com/ (accessed June 2006).
    • France
    • Bayesia, S.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.