메뉴 건너뛰기




Volumn , Issue , 2000, Pages 564-569

A Unified Bias-Variance Decomposition for Zero-One and Squared Loss

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECOMPOSITION; LEARNING ALGORITHMS;

EID: 85158077035     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (109)

References (15)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting and variants
    • Bauer, E., and Kohavi, R. 1999. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36:105-142.
    • (1999) Machine Learning , vol.36 , pp. 105-142
    • Bauer, E.1    Kohavi, R.2
  • 2
    • 0003408496 scopus 로고    scopus 로고
    • Machine-readable data repository, Department of Information and Computer Science, University of California at Irvine, Irvine, CA
    • Blake, C., and Merz, C. J. 2000. UCI repository of machine learning databases. Machine-readable data repository, Department of Information and Computer Science, University of California at Irvine, Irvine, CA. http://www.ics.uci.edu/-~mlearn/MLRepository.html.
    • (2000) UCI repository of machine learning databases
    • Blake, C.1    Merz, C. J.2
  • 3
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L. 1996a. Bagging predictors. Machine Learning 24:123-140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 4
    • 0003619255 scopus 로고    scopus 로고
    • Technical Report 460, Statistics Department, University of California at Berkeley, Berkeley, CA
    • Breiman, L. 1996b. Bias, variance and arcing classifiers. Technical Report 460, Statistics Department, University of California at Berkeley, Berkeley, CA.
    • (1996) Bias, variance and arcing classifiers
    • Breiman, L.1
  • 5
    • 0004198448 scopus 로고    scopus 로고
    • Technical Report 486, Statistics Department, University of California at Berkeley, Berkeley, CA
    • Breiman, L. 1997. Arcing the edge. Technical Report 486, Statistics Department, University of California at Berkeley, Berkeley, CA.
    • (1997) Arcing the edge
    • Breiman, L.1
  • 6
    • 0031269184 scopus 로고    scopus 로고
    • On the optimality of the simple Bayesian classifier under zero-one loss
    • Domingos, P., and Pazzani, M. 1997. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29:103-130.
    • (1997) Machine Learning , vol.29 , pp. 103-130
    • Domingos, P.1    Pazzani, M.2
  • 7
    • 0012937288 scopus 로고    scopus 로고
    • Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA
    • Domingos, P. 2000. A unified bias-variance decomposition. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA.
    • (2000) A unified bias-variance decomposition
    • Domingos, P.1
  • 8
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1 - loss, and the curse-of-dimensionality
    • Friedman, J. H. 1997. On bias, variance, 0/1 - loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery 1:55-77.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 55-77
    • Friedman, J. H.1
  • 10
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • Holte, R. C. 1993. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11:63-91.
    • (1993) Machine Learning , vol.11 , pp. 63-91
    • Holte, R. C.1
  • 15
    • 0003667773 scopus 로고    scopus 로고
    • Technical report, Department of Preventive Medicine and Biostatistics and Department of Statistics, University of Toronto, Toronto, Canada
    • Tibshirani, R. 1996. Bias, variance and prediction error for classification rules. Technical report, Department of Preventive Medicine and Biostatistics and Department of Statistics, University of Toronto, Toronto, Canada.
    • (1996) Bias, variance and prediction error for classification rules
    • Tibshirani, R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.