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Volumn 9, Issue , 2008, Pages 521-540

Estimating the confidence interval for prediction errors of support vector machine classifiers

Author keywords

K fold cross validation; Model evaluation; Perturbation resampling; Prediction errors; Support vector machine

Indexed keywords

COMPUTER SIMULATION; ERROR ANALYSIS; MATHEMATICAL MODELS; SUPPORT VECTOR MACHINES;

EID: 41549142154     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (57)

References (39)
  • 2
    • 84925604888 scopus 로고    scopus 로고
    • No unbiased estimator of the variance of k-fold cross-validation
    • Y. Bengio and Y. Grandvalet. No unbiased estimator of the variance of k-fold cross-validation. Journal of Machine Learning Research, 5:1089-1105, 2004.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1089-1105
    • Bengio, Y.1    Grandvalet, Y.2
  • 4
    • 24144432825 scopus 로고    scopus 로고
    • Semiparametric Box-Cox power transformation models for censored survival observations
    • T. Cai, L. Tian, and L. J. Wei. Semiparametric Box-Cox power transformation models for censored survival observations. Biometrika, 92:619-632, 2005.
    • (2005) Biometrika , vol.92 , pp. 619-632
    • Cai, T.1    Tian, L.2    Wei, L.J.3
  • 7
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10:1895-1923, 1998.
    • (1998) Neural Computation , vol.10 , pp. 1895-1923
    • Dietterich, T.1
  • 9
    • 0002344794 scopus 로고
    • Bootstrap methods: Another look at the jackknife
    • B. Efron. Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7:1-26,1979.
    • (1979) The Annals of Statistics , vol.7 , pp. 1-26
    • Efron, B.1
  • 10
    • 80053264999 scopus 로고
    • How biased is the apparent error rate of a prediction rule
    • B. Efron. How biased is the apparent error rate of a prediction rule. Journal of the American Statistical Association, 81:461-470, 1986.
    • (1986) Journal of the American Statistical Association , vol.81 , pp. 461-470
    • Efron, B.1
  • 12
    • 0004183412 scopus 로고
    • Cross-validation and the bootstrap: Estimating the error rate of a prediction rule
    • Technical report, Stanford University
    • B. Efron and R. Tibshirani. Cross-validation and the bootstrap: Estimating the error rate of a prediction rule. Technical report, Stanford University, 1995.
    • (1995)
    • Efron, B.1    Tibshirani, R.2
  • 14
    • 18744413287 scopus 로고    scopus 로고
    • Estimating misclassification error with small samples via bootstrap cross-validation
    • W. J. Fu, R. J. Carroll, and S. Wang. Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics, 21:1979-1986, 2005.
    • (2005) Bioinformatics , vol.21 , pp. 1979-1986
    • Fu, W.J.1    Carroll, R.J.2    Wang, S.3
  • 16
    • 21844518259 scopus 로고
    • On general resampling algorithms and their performance in distribution estimation
    • P. Hall and E. Mammen. On general resampling algorithms and their performance in distribution estimation. The Annals of Statistics, 22:2011-2030, 1994.
    • (1994) The Annals of Statistics , vol.22 , pp. 2011-2030
    • Hall, P.1    Mammen, E.2
  • 17
    • 0040605177 scopus 로고
    • Asymptotics for minimizers of convex processes
    • Technical report, Yale University
    • N. L. Hjort and D. Pollard. Asymptotics for minimizers of convex processes. Technical report, Yale University, 1993.
    • (1993)
    • Hjort, N.L.1    Pollard, D.2
  • 18
    • 0033566418 scopus 로고    scopus 로고
    • Algorithmic stability and sanity check bounds for leave-one-out crossvalidation
    • M. Kearns and D. Ron. Algorithmic stability and sanity check bounds for leave-one-out crossvalidation. Neural Computation, 11:1427-1453, 1999.
    • (1999) Neural Computation , vol.11 , pp. 1427-1453
    • Kearns, M.1    Ron, D.2
  • 19
    • 41549124215 scopus 로고    scopus 로고
    • R. Y Liu. Bootstrap procedures under some non-i.i.d. models. The Annals of Statistics, 16:16961708, 1988.
    • R. Y Liu. Bootstrap procedures under some non-i.i.d. models. The Annals of Statistics, 16:16961708, 1988.
  • 21
    • 25144494760 scopus 로고    scopus 로고
    • Prediction error estimation: A comparison of resampling methods
    • A. Molinaro, R. Simon, and R. Pfeiffer. Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21:3301-3307, 2005.
    • (2005) Bioinformatics , vol.21 , pp. 3301-3307
    • Molinaro, A.1    Simon, R.2    Pfeiffer, R.3
  • 22
    • 0042847140 scopus 로고    scopus 로고
    • Inference for the generalization error
    • C. Nadeau and Y Bengio. Inference for the generalization error. Machine Learning, 52:239-281, 2003.
    • (2003) Machine Learning , vol.52 , pp. 239-281
    • Nadeau, C.1    Bengio, Y.2
  • 23
    • 70350096085 scopus 로고
    • Large sample estimation and hypothesis testing
    • D. McFadden and R. Engler, editors, Amsterdam: North Holland
    • W. Newey and D. McFadden. Large sample estimation and hypothesis testing. In D. McFadden and R. Engler, editors, Handbook of Econometrics IV, pages 2113-2245. Amsterdam: North Holland, 1994.
    • (1994) Handbook of Econometrics IV , pp. 2113-2245
    • Newey, W.1    McFadden, D.2
  • 24
    • 0033827606 scopus 로고    scopus 로고
    • Baseline human immunodeficiency virus type I phenotype, genotype, and RNA response after switching from long-term hard-capsule saquinavir to indinavir or soft-gel-capsule saquinavir in AIDS clinical trials group protocol 333
    • M. F. Para, D. V. Glidden, R. Coombs, A. Collier, J. Condra, C. Craig, R. Basse«, R. Leavitt, S. Snyder, V. J. McAuliffe, and C. Boucher. Baseline human immunodeficiency virus type I phenotype, genotype, and RNA response after switching from long-term hard-capsule saquinavir to indinavir or soft-gel-capsule saquinavir in AIDS clinical trials group protocol 333. Journal of Infectious Diseases, 182:733-743, 2000.
    • (2000) Journal of Infectious Diseases , vol.182 , pp. 733-743
    • Para, M.F.1    Glidden, D.V.2    Coombs, R.3    Collier, A.4    Condra, J.5    Craig, C.6    Basse«, R.7    Leavitt, R.8    Snyder, S.9    McAuliffe, V.J.10    Boucher, C.11
  • 25
    • 3843056658 scopus 로고    scopus 로고
    • Estimating subject-specific survival functions under the accelerated failure time model
    • Y. Park and L. J. Wei. Estimating subject-specific survival functions under the accelerated failure time model. Biometrika, 90:717-723, 2003.
    • (2003) Biometrika , vol.90 , pp. 717-723
    • Park, Y.1    Wei, L.J.2
  • 27
    • 84971936861 scopus 로고
    • Asymptotics for least absolute deviation regression estimators
    • D. Pollard. Asymptotics for least absolute deviation regression estimators. Econometric Theory, 7: 186-199, 1991.
    • (1991) Econometric Theory , vol.7 , pp. 186-199
    • Pollard, D.1
  • 28
    • 84890445089 scopus 로고    scopus 로고
    • Overriding in making comparisons between variable selection methods
    • J. Reunanen. Overriding in making comparisons between variable selection methods. Journal of Machine Learning Research, 3:1371-1382, 2003.
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1371-1382
    • Reunanen, J.1
  • 30
    • 0000135972 scopus 로고
    • The bayesian bootstrap
    • D. Rubin. The bayesian bootstrap. The Annals of Statistics, 9:130-134, 1981.
    • (1981) The Annals of Statistics , vol.9 , pp. 130-134
    • Rubin, D.1
  • 34
    • 0036749277 scopus 로고    scopus 로고
    • Support vector machines are universally consistent
    • I. Steinwart. Support vector machines are universally consistent. Journal of Complexity, 18:768-779, 2002.
    • (2002) Journal of Complexity , vol.18 , pp. 768-779
    • Steinwart, I.1
  • 38
    • 33644860703 scopus 로고    scopus 로고
    • Bias in error estimation when using cross-validation for model selection
    • S. Varma and R. Simon. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7:91, 2006.
    • (2006) BMC Bioinformatics , vol.7 , pp. 91
    • Varma, S.1    Simon, R.2
  • 39
    • 0001673027 scopus 로고
    • Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion)
    • C. F. J. Wu. Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion). The Annals of Statistics, 14:1261-1295, 1986.
    • (1986) The Annals of Statistics , vol.14 , pp. 1261-1295
    • Wu, C.F.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.