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Volumn 5519 LNCS, Issue , 2009, Pages 509-518

Boosting support vector machines successfully

Author keywords

Boosting; Local models; Support vector machines

Indexed keywords

BOOSTING; COMPUTATIONAL COSTS; GLOBAL MODELS; LARGE DATASETS; LOCAL MODEL; LOCAL MODELS; MODEL STABILITY; MULTIPLE MODELS; PREDICTIVE ACCURACY; PREDICTIVE PERFORMANCE;

EID: 70349332798     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02326-2_51     Document Type: Conference Paper
Times cited : (10)

References (18)
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    • Zenobi, G., Cunningham, P.: Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS, vol. 2167, pp. 576-587. Springer, Heidelberg (2001)
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    • Lam, L.: Classifier Combinations. Implementations and Theoretical Issues. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, 1857, pp. 77-86. Springer, Heidelberg (2000)
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  • 6
    • 0004322632 scopus 로고    scopus 로고
    • Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines
    • Technical Report MST-TR-98-14, Microsoft Research
    • Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MST-TR-98-14, Microsoft Research (1998)
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    • Tao, D.1    Tang, X.2    Li, X.3    Wu, X.4
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    • A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement
    • Ting, K.M., Zheng, Z.: A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement. Computational Intelligence 19(2), 186-200 (2003)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.