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Volumn 2375, Issue , 2002, Pages 319-333

The consistency of greedy algorithms for classification

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; COMPUTATION THEORY; EXPONENTIAL FUNCTIONS; ITERATIVE METHODS;

EID: 84937440094     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-45435-7_22     Document Type: Conference Paper
Times cited : (25)

References (20)
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    • W. Jiang. Process consistency for adaboost. Technical Report 00-05, Department of Statistics, Northwestern University, 2000.
    • (2000) Process Consistency for Adaboost
    • Jiang, W.1
  • 9
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    • Empirical margin distributions and bounding the generalization error of combined classifiers
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    • To appear
    • S. Mannor and R. Meir. On the existence of weak learners and applications to boosting. Machine Learning, 2002. To appear.
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    • Functional gradient techniques for combining hypotheses
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    • (2000) Advances in Large Margin Classifiers
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    • 0033732457 scopus 로고    scopus 로고
    • On the optimality of neural network approximation using incremental algorithms
    • R. Meir and V. Maiorov. On the optimality of neural network approximation using incremental algorithms. IEEE Trans. Neural Networks, 11(2):323-337, 2000.
    • (2000) IEEE Trans. Neural Networks , vol.11 , Issue.2 , pp. 323-337
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    • Minimax nonparametric classification - Patr i: Rates of convergence
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    • Yang, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.