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Volumn 2, Issue , 2007, Pages 663-668

Learning by bagging and adaboost based on support vector machine

Author keywords

AdaBoost; Bagging; Ensemble classifiers; Machine learning; Support vector machine

Indexed keywords

ADAPTIVE SYSTEMS; BENCHMARKING; DATA STRUCTURES; PARAMETER ESTIMATION;

EID: 39749159770     PISSN: 19354576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/INDIN.2007.4384852     Document Type: Conference Paper
Times cited : (4)

References (19)
  • 1
    • 10444221886 scopus 로고    scopus 로고
    • Diversity Creation Methods: A Survey and Categorisation,
    • G. Brown, "Diversity Creation Methods: A Survey and Categorisation,". Journal of Information Fusion, vol.6, no.l, pp. 5-20 2005.
    • (2005) Journal of Information Fusion , vol.6 , Issue.L , pp. 5-20
    • Brown, G.1
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • 1
    • Leo Breiman, "Bagging predictors," Machine Learning, vol. 24, pp.123-140, 1996. 1.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 5
    • 0142025124 scopus 로고    scopus 로고
    • Constructing support vector machine ensemble
    • Kim H.-C, Pang S, Je H.-M, "Constructing support vector machine ensemble" Pattern Recognition, vol.36, pp.2757-2767, 2005.
    • (2005) Pattern Recognition , vol.36 , pp. 2757-2767
    • Kim, H.-C.1    Pang, S.2    Je, H.-M.3
  • 7
    • 33749573711 scopus 로고    scopus 로고
    • Feature Selection for Bagging of Support Vector Machines
    • Guo-Zheng Li, and Tian-Yu Liu, "Feature Selection for Bagging of Support Vector Machines," in Conf PRICAI2006, pp.271-277, 2006.
    • (2006) Conf PRICAI2006 , pp. 271-277
    • Li, G.-Z.1    Liu, T.-Y.2
  • 8
    • 33745937896 scopus 로고    scopus 로고
    • Xuchun Li, Lei Wang, Sung.E, A study of AdaBoost with SVM based weak learners, Neural Networks (UCNNOS), pp.196-201, 2005.
    • Xuchun Li, Lei Wang, Sung.E, "A study of AdaBoost with SVM based weak learners," Neural Networks (UCNNOS), pp.196-201, 2005.
  • 9
    • 33847331702 scopus 로고    scopus 로고
    • Boosting of support vector machines with application to editing
    • Rangel. P, Lozano. F, "Boosting of support vector machines with application to editing," in Proc. Machine Learning and Applications, pp.6-11, 2005.
    • (2005) Proc. Machine Learning and Applications , pp. 6-11
    • Rangel, P.1    Lozano, F.2
  • 11
    • 33745789237 scopus 로고    scopus 로고
    • Yang Liu, Aijun An, Xiang ji Huang, Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles, in Proc. PAKDD 2006, pp.107-118.
    • Yang Liu, Aijun An, Xiang ji Huang, "Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles," in Proc. PAKDD 2006, pp.107-118.
  • 12
    • 33745739433 scopus 로고    scopus 로고
    • Demonstrating the stability of support vector machines for classification
    • I. Buciu, "Demonstrating the stability of support vector machines for classification," Signal Processing, vol.86, pp.2364-2380, 2006.
    • (2006) Signal Processing , vol.86 , pp. 2364-2380
    • Buciu, I.1
  • 14
    • 10244264678 scopus 로고    scopus 로고
    • SVM training with duplicated samples and its application in SVM-based ensemble methods
    • Junshui Ma, Ashok Krishnamurthy, Stanley Ahalt, "SVM training with duplicated samples and its application in SVM-based ensemble methods," Neurocompuling, vol. 61, pp.445-459, 2004.
    • (2004) Neurocompuling , vol.61 , pp. 445-459
    • Ma, J.1    Krishnamurthy, A.2    Ahalt, S.3
  • 15
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • T. G. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization," Machine Learning, vol. 40, no. 2, pp. 139-157, 2000.
    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.G.1
  • 16
    • 33749376860 scopus 로고    scopus 로고
    • An Effective Combination based on Class-Wise Expertise of Diverse Classifiers for Predictive Toxicology Data Mining
    • Daniel Neagu, Gongde Guo, "An Effective Combination based on Class-Wise Expertise of Diverse Classifiers for Predictive Toxicology Data Mining," in Conf. The Second International Conference on Advanced Data Mining and Applications, pp. 165-172, 2006.
    • (2006) Conf. The Second International Conference on Advanced Data Mining and Applications , pp. 165-172
    • Neagu, D.1    Guo, G.2
  • 17
    • 26444521653 scopus 로고    scopus 로고
    • Ensemble Learning with Biased Classifiers: The Triskel Algorithm
    • Multiple Classifier Systems, pp
    • Andreas Heß, Rinat Khoussainov, Nicholas Kushmerick, "Ensemble Learning with Biased Classifiers: The Triskel Algorithm," in Conf. MCS200S : Multiple Classifier Systems, pp.226-235, 2005.
    • (2005) Conf. MCS200S , pp. 226-235
    • Heß, A.1    Khoussainov, R.2    Kushmerick, N.3
  • 18
    • 84886999404 scopus 로고    scopus 로고
    • Boosting by weighting boundary and erroneous samples
    • V.G.Verdejo, "Boosting by weighting boundary and erroneous samples," in Proc. 13th European Symp on ANN, pp. 85-90, 2005.
    • (2005) Proc. 13th European Symp on ANN , pp. 85-90
    • Verdejo, V.G.1
  • 19
    • 39749102036 scopus 로고    scopus 로고
    • http://ida.first.fraunhofer.de/projects/bench/


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.