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Volumn 4477 LNCS, Issue PART 1, 2007, Pages 105-112

Performance analysis of classifier ensembles: Neural networks versus nearest neighbor rule

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; PROBLEM SOLVING;

EID: 38149020642     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72847-4_15     Document Type: Conference Paper
Times cited : (6)

References (20)
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    • Bagging predictors
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    • Arcing classifiers
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  • 8
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    • Negative correlation learning and the ambiguity family of ensemble methods
    • Guilford, UK, pp
    • Brown, G., Wyatt, J.: Negative correlation learning and the ambiguity family of ensemble methods, In: Proc. Intl. Workshop on Multiple Classifier Systems. Guilford, UK, pp. 266-275 (2003)
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    • Brown, G.1    Wyatt, J.2
  • 9
    • 0010327861 scopus 로고
    • Nearest Neighbor Norms: NN Pattern Classification Techniques
    • Dasarathy, B.V, ed, Los Alamos, CA
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    • (1991) IEEE Computer Society Press
  • 10
    • 0031361611 scopus 로고    scopus 로고
    • Machine learning research: Four current directions
    • Dietterich, G.T.: Machine learning research: four current directions. AI Magazine 18, 97-136 (1997)
    • (1997) AI Magazine , vol.18 , pp. 97-136
    • Dietterich, G.T.1
  • 11
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mapping by neural networks
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  • 13
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    • Using measures of similarity and inclusion for multiple classifier fusion by decision templates
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.