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Volumn 5, Issue 2, 2002, Pages 201-209

Combining different methods and numbers of weak decision trees

Author keywords

Bagging; Boosting; Decision trees; Ensemble learning; Random subspaces

Indexed keywords


EID: 0036080042     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s100440200018     Document Type: Article
Times cited : (12)

References (35)
  • 1
    • 84956973748 scopus 로고    scopus 로고
    • Data complexity analysis for classifier combination
    • Cambridge, UK. Lecture Notes in Computer Science, Springer-Verlag
    • Ho TK. Data complexity analysis for classifier combination. Proceedings 2nd International Workshop of Multiple Classifier System, Cambridge, UK. Lecture Notes in Computer Science, Springer-Verlag, 2001; 2096:53-67
    • (2001) Proceedings 2nd International Workshop of Multiple Classifier System , vol.2096 , pp. 53-67
    • Ho, T.K.1
  • 3
    • 0029230267 scopus 로고
    • A method of combining multiple experts for the recognition of unconstrained handwritten numerals
    • Huang YS, Suen CY. A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans Pattern Analysis and Machine Intelligence 1995; 17(1)
    • (1995) IEEE Trans Pattern Analysis and Machine Intelligence , vol.17 , Issue.1
    • Huang, Y.S.1    Suen, C.Y.2
  • 5
    • 84867038166 scopus 로고    scopus 로고
    • Classifier combinations: Implementations and theoretical issues
    • Cagliari, Italy. Lecture Notes in Computer Science, Springer-Verlag
    • Lam L. Classifier combinations: implementations and theoretical issues. Proceedings 1st International Workshop of Multiple Classifier System, Cagliari, Italy. Lecture Notes in Computer Science, Springer-Verlag, 2000; 1857:77-86
    • (2000) Proceedings 1st International Workshop of Multiple Classifier System , vol.1857 , pp. 77-86
    • Lam, L.1
  • 6
    • 0026860706 scopus 로고
    • Methods of combining multiple classifiers and their applications to handwriting recogntion
    • Xu L, Krzyzak A, Suen CY. Methods of combining multiple classifiers and their applications to handwriting recogntion. IEEE Trans Systems, Man and Cybernetics 1992; 22(3):418-435
    • (1992) IEEE Trans Systems, Man and Cybernetics , vol.22 , Issue.3 , pp. 418-435
    • Xu, L.1    Krzyzak, A.2    Suen, C.Y.3
  • 7
    • 0030235637 scopus 로고    scopus 로고
    • Error reduction through learning multiple descriptions
    • Ali KM, Pazzani MJ. Error reduction through learning multiple descriptions. Machine Learning 1996; 24:173-202
    • (1996) Machine Learning , vol.24 , pp. 173-202
    • Ali, K.M.1    Pazzani, M.J.2
  • 9
    • 0030736561 scopus 로고    scopus 로고
    • Combinations of weak classifiers
    • Ji and Ma. Combinations of weak classifiers. IEEE Trans Neural Network 1997; 7(1):32-42
    • (1997) IEEE Trans Neural Network , vol.7 , Issue.1 , pp. 32-42
    • Ji1    Ma2
  • 11
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Schapire RE. The strength of weak learnability. Machine Learning 1990; 5:197-227
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 12
    • 84944215019 scopus 로고    scopus 로고
    • Input decimation ensembles: Decorrelating through dimensionality
    • Cambridge, UK. Lecture Notes in Computer Science, Springer-Verlag
    • Oza NC, Turner K. Input decimation ensembles: Decorrelating through dimensionality. Proceedings 2nd International Workshop of Multiple Classifier System, Cambridge, UK. Lecture Notes in Computer Science, Springer-Verlag, 2001; 2096:238-247
    • (2001) Proceedings 2nd International Workshop of Multiple Classifier System , vol.2096 , pp. 238-247
    • Oza, N.C.1    Turner, K.2
  • 14
    • 0028257732 scopus 로고
    • Democracy in neural nets: Voting schemes for classification
    • Battiti R, Colla AM. Democracy in neural nets: voting schemes for classification. Neural Networks 1995; 7(4):691-708
    • (1995) Neural Networks , vol.7 , Issue.4 , pp. 691-708
    • Battiti, R.1    Colla, A.M.2
  • 15
    • 84867038939 scopus 로고    scopus 로고
    • Experiments with classifier combining rules
    • Cagliari, Italy. Lecture Notes in Computer Science, Springer-Verlag
    • Duin RPW, Tax DMJ. Experiments with classifier combining rules. Proceedings 1st International Workshop of Multiple Classifier System, Cagliari, Italy. Lecture Notes in Computer Science, Springer-Verlag, 2000; 1857:16-29
    • (2000) Proceedings 1st International Workshop of Multiple Classifier System , vol.1857 , pp. 16-29
    • Duin, R.P.W.1    Tax, D.M.J.2
  • 16
    • 0031238275 scopus 로고    scopus 로고
    • Application of majority voting to pattern recognition: An analysis of its behavior and performance
    • Lam L, Suen CY. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans Systems, Man and Cybernetics 1997; 27(5)
    • (1997) IEEE Trans Systems, Man and Cybernetics , vol.27 , Issue.5
    • Lam, L.1    Suen, C.Y.2
  • 17
    • 0345159806 scopus 로고
    • Putting it all together: Methods for combining neural networks
    • Perrone M. Putting it all together: methods for combining neural networks. Advances in Neural Information Processing Systems 6, 1994; 1188-1189
    • (1994) Advances in Neural Information Processing Systems , vol.6 , pp. 1188-1189
    • Perrone, M.1
  • 20
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of online learning and an application to boosting
    • Freund and Schapire. A decision-theoretic generalization of online learning and an application to boosting. J Computer and System Sciences 1997; 55(1):119-139
    • (1997) J Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund1    Schapire2
  • 21
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
    • Dietterich TG. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Machine Learning 2000; 40:139-157
    • (2000) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.G.1
  • 25
  • 26
    • 0006001358 scopus 로고    scopus 로고
    • Random forests - Random features
    • Statistics Department, University of California, Berkeley, CA 94720, September
    • Breiman L. Random forests - random features. Technical Report 567, Statistics Department, University of California, Berkeley, CA 94720, September 1999
    • (1999) Technical Report , vol.567
    • Breiman, L.1
  • 28
    • 0003408496 scopus 로고    scopus 로고
    • University of California, Department of Information and Computer Science
    • Blake C, Keogh E, Merz CJ. Uci respository of machine learning databases. [http://www.ics.uci.edu/mlearn/MLRepository.html]. University of California, Department of Information and Computer Science, 1998
    • (1998) Uci Respository of Machine Learning Databases
    • Blake, C.1    Keogh, E.2    Merz, C.J.3
  • 29
    • 0346786584 scopus 로고    scopus 로고
    • Arching classifiers
    • Breiman L. Arching classifiers. Annals of statistics 1998; 26:801-849
    • (1998) Annals of Statistics , vol.26 , pp. 801-849
    • Breiman, L.1
  • 33
    • 27144463192 scopus 로고    scopus 로고
    • On comparing classifiers: Pitfalls to avoid and a recommended approach
    • Salzberg S. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1997; 1:317-327
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 317-327
    • Salzberg, S.1
  • 34
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • Dietterich TG. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 1998; 10:1895-1923
    • (1998) Neural Computation , vol.10 , pp. 1895-1923
    • Dietterich, T.G.1
  • 35
    • 84994037050 scopus 로고    scopus 로고
    • Dynamic classifier selection based on multiple classifier behaviour
    • Giacento G, Roli F. Dynamic classifier selection based on multiple classifier behaviour. Pattern Recognition Letters 2001; 34(9):179-181
    • (2001) Pattern Recognition Letters , vol.34 , Issue.9 , pp. 179-181
    • Giacento, G.1    Roli, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.