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Volumn 6, Issue , 2003, Pages 3510-3512

Random forests for land cover classification

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); ERROR ANALYSIS; NEURAL NETWORKS; SPURIOUS SIGNAL NOISE; STRUCTURAL GEOLOGY; TREES (MATHEMATICS);

EID: 0242709836     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (41)

References (18)
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    • An empirical comparison of voting classification algorithms
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    • Bauer, E.1    Kohavi, R.2
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    • Arching Classifiers
    • L. Breiman, Arching Classifiers. Annals of statistics, 26, 801-824, 1998.
    • (1998) Annals of Statistics , vol.26 , pp. 801-824
    • Breiman, L.1
  • 6
    • 0006502062 scopus 로고    scopus 로고
    • Randomisation outputs to increase prediction accuracy
    • Statistics department, University of California, Berkeley
    • L. Breiman, Randomisation outputs to increase prediction accuracy. Technical report 518, Statistics department, University of California, Berkeley, 1998a.
    • (1998) Technical Report , vol.518
    • Breiman, L.1
  • 7
    • 0041382385 scopus 로고    scopus 로고
    • Random forests
    • Statistics department, University of California, Berkeley
    • L. Breiman, Random forests, Technical report, Statistics department, University of California, Berkeley, 1999.
    • (1999) Technical Report
    • Breiman, L.1
  • 8
    • 0013228784 scopus 로고    scopus 로고
    • Some infinity theory of predictor ensembles
    • Statistics department, University of California, Berkeley
    • L. Breiman, Some infinity theory of predictor ensembles. Technical report 577, Statistics department, University of California, Berkeley, 2000.
    • (2000) Technical Report , vol.577
    • Breiman, L.1
  • 10
    • 0001823341 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision tree: Bagging, Boosting, and Randomization
    • T. G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision tree: Bagging, Boosting, and Randomization. Machine Learning, 1-22, 1998.
    • (1998) Machine Learning , pp. 1-22
    • Dietterich, T.G.1
  • 11
    • 0003660631 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • Statistical department, Stanford University
    • J. Friedman, T. hastie and R. Tibshirani, Additive logistic regression: a statistical view of boosting. Technical report, Statistical department, Stanford University, 1998.
    • (1998) Technical Report
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 13
    • 0033100735 scopus 로고    scopus 로고
    • Maximizing land cover classification accuracies produced by decision tree at continental to global scales
    • M. A. Friedl, C. E. Brodley, and A. H. Strahler, Maximizing land cover classification accuracies produced by decision tree at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing. 37, 969-977, 1999
    • (1999) IEEE Transactions on Geoscience and Remote Sensing , vol.37 , pp. 969-977
    • Friedl, M.A.1    Brodley, C.E.2    Strahler, A.H.3
  • 14
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    • Ensembles of neural networks for soft classification of remote sensing images
    • European Network for Fuzzy Logic and Uncertainty Modelling in Information Technology, Bari, Italy
    • G. Giacinto, and F. Roli, Ensembles of neural networks for soft classification of remote sensing images, Proceedings of the European Symposium on Intelligent Techniques, European Network for Fuzzy Logic and Uncertainty Modelling in Information Technology, Bari, Italy, 166-170, 1997.
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    • Giacinto, G.1    Roli, F.2
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    • Comparison and combination of statistical and neural networks algorithms for remote-sensing image classification
    • Austin, J., Kanellopoulos, I., Roli, F. and Wilkinson G. (Eds.), Berlin: Springer-Verlag
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.