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Volumn 73, Issue 1-3, 2009, Pages 106-114

Bagging and AdaBoost algorithms for vector quantization

Author keywords

Bagging; Boosting; Ensemble learning; K means; Vector quantization

Indexed keywords

ADABOOST; ADABOOST ALGORITHM; BAGGING; BOOSTING; DATA SETS; ENSEMBLE LEARNING; ENSEMBLE LEARNING ALGORITHM; K-MEANS; LEARNING DATA; SIMULATION RESULT; WEAK LEARNER; WEIGHTED AVERAGES;

EID: 70350712267     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.02.020     Document Type: Article
Times cited : (14)

References (13)
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    • Freund, Y.1    Schapire, R.E.2
  • 5
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    • A hybrid learning approach to self-organizing neural network for vector quantization
    • Fukumoto S., Shigei N., Maeda M., and Miyajima H. A hybrid learning approach to self-organizing neural network for vector quantization. IEICE Transactions on Fundamentals E86-A 9 (2003) 2280-2286
    • (2003) IEICE Transactions on Fundamentals , vol.E86-A , Issue.9 , pp. 2280-2286
    • Fukumoto, S.1    Shigei, N.2    Maeda, M.3    Miyajima, H.4
  • 8
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  • 10
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    • Analysis of ensemble learning using simple perceptrons on online learning theory
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  • 12
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    • Numerical evaluation of incremental vector quantization using stochastic relaxation
    • Shigei N., Miyajima H., and Maeda M. Numerical evaluation of incremental vector quantization using stochastic relaxation. IEICE Transactions on Fundamentals E87-A 9 (2004) 2364-2371
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  • 13
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.