메뉴 건너뛰기




Volumn 34, Issue 4, 2010, Pages 291-308

Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: A comparative study

Author keywords

Bayesian belief network; Computer vision; Context; Object recognition

Indexed keywords

BAYESIAN BELIEF NETWORKS; COMPARATIVE STUDIES; CONSTRAINT-BASED; CONTEXT; DATA SETS; EMPIRICAL RESULTS; HILL CLIMBING; HYBRID ALGORITHMS; LEARNING TIME; LINEAR CORRELATION; MARKOV BLANKETS; MAX-MIN; PREDICTION PERFORMANCE; STRUCTURE LEARNING ALGORITHM;

EID: 78650171699     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-010-9176-8     Document Type: Article
Times cited : (16)

References (50)
  • 2
    • 1842815776 scopus 로고    scopus 로고
    • A comparison of learning algorithms for Bayesian networks: A case study based on data from an emergency medical service
    • DOI 10.1016/j.artmed.2003.11.002, PII S0933365703001325
    • S Acid LM de Campos JM Fernández-Luna S Rodríguez JM Rodríguez JL Salcedo 2004 A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service Artif Intell Med 30 3 215 232 10.1016/j.artmed.2003.11.002 Bayesian Networks in Biomedicince and Health-Care (Pubitemid 38471894)
    • (2004) Artificial Intelligence in Medicine , vol.30 , Issue.3 , pp. 215-232
    • Acid, S.1    De Campos, L.M.2    Fernandez-Luna, J.M.3    Rodriguez, S.4    Maria Rodriguez, J.5    Luis Salcedo, J.6
  • 5
    • 29344455317 scopus 로고    scopus 로고
    • A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
    • AAAI Press; MIT Press; Menlo Park; Cambridge, 1999
    • Brown L, Tsamardinos I, Aliferis C (2005) A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms. In: Proceedings of the national conference on artificial intelligence, vol. 20. AAAI Press; MIT Press; Menlo Park; Cambridge, 1999, pp 739
    • (2005) Proceedings of the National Conference on Artificial Intelligence , vol.20 , pp. 739
    • Brown, L.1    Tsamardinos, I.2    Aliferis, C.3
  • 6
    • 0036567524 scopus 로고    scopus 로고
    • Learning Bayesian networks from data: An information-theory based approach
    • DOI 10.1016/S0004-3702(02)00191-1, PII S0004370202001911
    • J Cheng R Greiner J Kelly D Bell W Liu 2002 Learning Bayesian networks from data: an information-theory based approach Artif Intell 137 43 90 0995.68114 10.1016/S0004-3702(02)00191-1 1906473 (Pubitemid 34405215)
    • (2002) Artificial Intelligence , vol.137 , Issue.1-2 , pp. 43-90
    • Cheng, J.1    Greiner, R.2    Kelly, J.3    Bell, D.4    Liu, W.5
  • 8
    • 84949434688 scopus 로고    scopus 로고
    • Learning Bayesian belief network classifiers: Algorithms and system
    • 10.1007/3-540-45153-6-14
    • J Cheng R Greiner 2001 Learning Bayesian belief network classifiers: algorithms and system Adv Artif Intell 2056/2001 141 151 10.1007/3-540-45153-6- 14
    • (2001) Adv Artif Intell , vol.2056 , Issue.2001 , pp. 141-151
    • Cheng, J.1    Greiner, R.2
  • 9
    • 33646107783 scopus 로고    scopus 로고
    • Large-sample learning of Bayesian networks is NP-Hard
    • 2248018
    • DM Chickering D Heckerman C Meek 2004 Large-sample learning of Bayesian networks is NP-Hard J Mach Learn Res 5 1287 1330 2248018
    • (2004) J Mach Learn Res , vol.5 , pp. 1287-1330
    • Chickering, D.M.1    Heckerman, D.2    Meek, C.3
  • 10
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • 0766.68109
    • GF Cooper E Herskovits 1992 A Bayesian method for the induction of probabilistic networks from data Mach Learn 9 4 309 347 0766.68109
    • (1992) Mach Learn , vol.9 , Issue.4 , pp. 309-347
    • Cooper, G.F.1    Herskovits, E.2
  • 11
    • 34948848213 scopus 로고    scopus 로고
    • Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models
    • Morgan Kaufmann Publishers Inc, Los Altos
    • Cowell RG (2001) Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models. In: UAI '01: Proceedings of the 17th conference in uncertainty in artificial Intelligence, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc, Los Altos, pp 91-97
    • (2001) UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, San Francisco, CA, USA , pp. 91-97
    • Cowell, R.G.1
  • 13
    • 3042815198 scopus 로고    scopus 로고
    • Bayesian networks and information retrieval: An introduction to the special issue
    • 10.1016/j.ipm.2004.03.001 Bayesian Networks and Information Retrieval
    • LM de Campos JM Fernández-Luna JF Huete 2004 Bayesian networks and information retrieval: an introduction to the special issue Inf Process Manag 40 5 727 733 10.1016/j.ipm.2004.03.001 Bayesian Networks and Information Retrieval
    • (2004) Inf Process Manag , vol.40 , Issue.5 , pp. 727-733
    • De Campos, L.M.1    Fernández-Luna, J.M.2    Huete, J.F.3
  • 14
    • 0031276011 scopus 로고    scopus 로고
    • Bayesian network classifiers
    • 0892.68077 10.1023/A:1007465528199
    • N Friedman D Geiger M Goldszmidt 1997 Bayesian network classifiers Mach Learn 29 2 131 163 0892.68077 10.1023/A:1007465528199
    • (1997) Mach Learn , vol.29 , Issue.2 , pp. 131-163
    • Friedman, N.1    Geiger, D.2    Goldszmidt, M.3
  • 16
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • 10.1089/106652700750050961
    • N Friedman M Linial I Nachman D Pe'er 2000 Using Bayesian networks to analyze expression data J Comput Biol 7 3-4 601 620 10.1089/106652700750050961
    • (2000) J Comput Biol , vol.7 , Issue.34 , pp. 601-620
    • Friedman, N.1    Linial, M.2    Nachman, I.3    Pe'Er, D.4
  • 18
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • 0831.68096
    • D Heckerman D Geiger DM Chickering 1995 Learning Bayesian networks: the combination of knowledge and statistical data Mach Learn 20 3 197 243 0831.68096
    • (1995) Mach Learn , vol.20 , Issue.3 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 20
    • 41649102642 scopus 로고    scopus 로고
    • Application of Bayesian belief network in reliable analysis for video deinterlacing
    • DOI 10.1109/TCE.2008.4470034
    • G Jeon R Falcon D Kim R Lee J Jeong 2008 Application of Bayesian belief network in reliable analysis for video deinterlacing IEEE Trans Consumer Electron 54 1 123 130 10.1109/TCE.2008.4470034 (Pubitemid 351479614)
    • (2008) IEEE Transactions on Consumer Electronics , vol.54 , Issue.1 , pp. 123-139
    • Jeon, G.1    Falcon, R.2    Kim, D.3    Lee, R.4    Jeong, J.5
  • 21
    • 63749120518 scopus 로고    scopus 로고
    • A Bayesian network learning algorithm based on independence test and ant colony optimization
    • 05732256 10.3724/SP.J.1004.2009.00281
    • J-Z Ji H-X Zhang R-B Hu C-N Liu 2009 A Bayesian network learning algorithm based on independence test and ant colony optimization Acta Automatica Sinica 35 3 281 288 05732256 10.3724/SP.J.1004.2009.00281
    • (2009) Acta Automatica Sinica , vol.35 , Issue.3 , pp. 281-288
    • Ji, J.-Z.1    Zhang, H.-X.2    Hu, R.-B.3    Liu, C.-N.4
  • 25
    • 0001927585 scopus 로고
    • On information and sufficiency
    • 0042.38403 10.1214/aoms/1177729694 39968
    • S Kullback R Leibler 1951 On information and sufficiency Ann Math Stat 22 1 79 86 0042.38403 10.1214/aoms/1177729694 39968
    • (1951) Ann Math Stat , vol.22 , Issue.1 , pp. 79-86
    • Kullback, S.1    Leibler, R.2
  • 26
    • 0028482006 scopus 로고
    • Learning Bayesian belief networks: An approach based on the MDL principle
    • 10.1111/j.1467-8640.1994.tb00166.x
    • W Lam F Bacchus 1994 Learning Bayesian belief networks: an approach based on the MDL principle Comput Intell 10 3 269 293 10.1111/j.1467-8640.1994. tb00166.x
    • (1994) Comput Intell , vol.10 , Issue.3 , pp. 269-293
    • Lam, W.1    Bacchus, F.2
  • 28
    • 33750475608 scopus 로고    scopus 로고
    • A Bayesian Belief Network for IT implementation decision support
    • DOI 10.1016/j.dss.2006.01.003, PII S0167923606000078
    • EJ Lauría PJ Duchessi 2006 A Bayesian belief network for it implementation decision support Decis Support Syst 42 3 1573 1588 10.1016/j.dss.2006.01.003 (Pubitemid 44648443)
    • (2006) Decision Support Systems , vol.42 , Issue.3 , pp. 1573-1588
    • Lauria, E.J.M.1    Duchessi, P.J.2
  • 29
    • 53849092081 scopus 로고    scopus 로고
    • Bayesian belief network for box-office performance: A case study on Korean movies
    • 10.1016/j.eswa.2007.09.042
    • KJ Lee W Chang 2009 Bayesian belief network for box-office performance: a case study on Korean movies Expert Syst Appl 36 1 280 291 10.1016/j.eswa.2007. 09.042
    • (2009) Expert Syst Appl , vol.36 , Issue.1 , pp. 280-291
    • Lee, K.J.1    Chang, W.2
  • 30
    • 33749434592 scopus 로고    scopus 로고
    • Technical Report 2004/PhLOF, PSI, LITIS Laboratory, INSA de Rouen, Avenue de l'Université-BP 8, 76801 Saint-Étienne-du-Rouvray Cedex
    • Leray P, Francois O (2004) BNT structure learning package: documentation and experiments. Technical Report 2004/PhLOF, PSI, LITIS Laboratory, INSA de Rouen, Avenue de l'Université-BP 8, 76801 Saint-Étienne-du-Rouvray Cedex
    • (2004) BNT Structure Learning Package: Documentation and Experiments
    • Leray, P.1    Francois, O.2
  • 31
    • 78650176523 scopus 로고    scopus 로고
    • A new Bayesian network structure for classification tasks
    • M Madden 2002 A new Bayesian network structure for classification tasks Artif Intell Cogn Sci 2464/2002 183 197
    • (2002) Artif Intell Cogn Sci , vol.2464 , Issue.2002 , pp. 183-197
    • Madden, M.1
  • 34
    • 34347345603 scopus 로고    scopus 로고
    • Ph. D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. Available as Technical Report CMU-CS-03-153
    • Margaritis D (2003) Learning Bayesian network model structure from data. Ph. D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. Available as Technical Report CMU-CS-03-153
    • (2003) Learning Bayesian Network Model Structure from Data
    • Margaritis, D.1
  • 35
    • 0003229133 scopus 로고    scopus 로고
    • The Bayes net toolbox for Matlab
    • KP Murphy 2001 The Bayes net toolbox for Matlab Comput Sci Stat 33 2 1024 1034
    • (2001) Comput Sci Stat , vol.33 , Issue.2 , pp. 1024-1034
    • Murphy, K.P.1
  • 38
    • 70349426182 scopus 로고    scopus 로고
    • Unsupervised training of Bayesian networks for data clustering
    • 1186.68387 10.1098/rspa.2009.0065 2524839
    • D Pham G Ruz 2009 Unsupervised training of Bayesian networks for data clustering Proc Royal Soci A 465 2109 2927 2948 1186.68387 10.1098/rspa.2009. 0065 2524839
    • (2009) Proc Royal Soci A , vol.465 , Issue.2109 , pp. 2927-2948
    • Pham, D.1    Ruz, G.2
  • 39
    • 39749186006 scopus 로고    scopus 로고
    • LabelMe: A database and web-based tool for image annotation
    • 10.1007/s11263-007-0090-8
    • BC Russell A Torralba KP Murphy WT Freeman 2008 LabelMe: a database and web-based tool for image annotation Int J Comput Vision 77 1-3 157 173 10.1007/s11263-007-0090-8
    • (2008) Int J Comput Vision , vol.77 , Issue.13 , pp. 157-173
    • Russell, B.C.1    Torralba, A.2    Murphy, K.P.3    Freeman, W.T.4
  • 40
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • 0379.62005 10.1214/aos/1176344136
    • G Schwarz 1978 Estimating the dimension of a model Ann Stat 6 2 461 464 0379.62005 10.1214/aos/1176344136
    • (1978) Ann Stat , vol.6 , Issue.2 , pp. 461-464
    • Schwarz, G.1
  • 43
    • 17444375107 scopus 로고    scopus 로고
    • Available at: AccessedonFebruary02,2010
    • Tape TG (2009) The area under an ROC curve. Available at: http://gim.unmc.edu/dxtests/roc3.htm. Accessed on February 02, 2010
    • (2009) The Area under An ROC Curve
    • Tape, T.G.1
  • 46
    • 33746035971 scopus 로고    scopus 로고
    • The max-min hill-climbing Bayesian network structure learning algorithm
    • DOI 10.1007/s10994-006-6889-7
    • I Tsamardinos L Brown C Aliferis 2006 The max- min hill-climbing Bayesian network structure learning algorithm Mach Learn 65 1 31 78 10.1007/s10994-006- 6889-7 (Pubitemid 44451193)
    • (2006) Machine Learning , vol.65 , Issue.1 , pp. 31-78
    • Tsamardinos, I.1    Brown, L.E.2    Aliferis, C.F.3
  • 49
    • 43249121828 scopus 로고    scopus 로고
    • Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
    • 10.1016/j.dss.2008.01.002 I.T. and Value Creation
    • ML Wong YY Guo 2008 Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm Decis Support Syst 45 2 368 383 10.1016/j.dss.2008.01.002 I.T. and Value Creation
    • (2008) Decis Support Syst , vol.45 , Issue.2 , pp. 368-383
    • Wong, M.L.1    Guo, Y.Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.