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      KCI등재

      Two-step LS-SVR for censored regression

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      https://www.riss.kr/link?id=A104127989

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      다국어 초록 (Multilingual Abstract)

      This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.
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      This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the l...

      This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

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      참고문헌 (Reference)

      1 Ying, Z. L., "Survival analysis with median regression models" 90 : 178-184, 1995

      2 Gunn, S. R., "Support vector machines for classi cation and regression, Technical report" Department of Electronics and Computer Science, Southamption 1998

      3 Vapnik, V. N., "Statistical learning theory" Wiley 1998

      4 Kimeldorf, G., "Some results on Tchebyche an spline functions" 33 : 82-95, 1981

      5 황창하, "Semiparametric support vector machine for accelerated failure time model" 한국데이터정보과학회 21 (21): 765-775, 2010

      6 심주용, "Semiparametric least squares support vector machine for accelerated failure time model" 한국통계학회 40 (40): 75-83, 2011

      7 Koul, H., "Regression analysis with randomly right censored data" 9 : 1276-1288, 1981

      8 Jin, Z., "Rank-based inference for the accelerated failure time model" 90 : 341-353, 2003

      9 Smola, A., "On a kernel-based method for pattern recognition, regression, approx-imation and operator inversion" 22 : 211-231, 1998

      10 Kaplan, E. L., "Nonparametric estimation from incomplete observations" 53 : 457-481, 1958

      1 Ying, Z. L., "Survival analysis with median regression models" 90 : 178-184, 1995

      2 Gunn, S. R., "Support vector machines for classi cation and regression, Technical report" Department of Electronics and Computer Science, Southamption 1998

      3 Vapnik, V. N., "Statistical learning theory" Wiley 1998

      4 Kimeldorf, G., "Some results on Tchebyche an spline functions" 33 : 82-95, 1981

      5 황창하, "Semiparametric support vector machine for accelerated failure time model" 한국데이터정보과학회 21 (21): 765-775, 2010

      6 심주용, "Semiparametric least squares support vector machine for accelerated failure time model" 한국통계학회 40 (40): 75-83, 2011

      7 Koul, H., "Regression analysis with randomly right censored data" 9 : 1276-1288, 1981

      8 Jin, Z., "Rank-based inference for the accelerated failure time model" 90 : 341-353, 2003

      9 Smola, A., "On a kernel-based method for pattern recognition, regression, approx-imation and operator inversion" 22 : 211-231, 1998

      10 Kaplan, E. L., "Nonparametric estimation from incomplete observations" 53 : 457-481, 1958

      11 Heuchenne, C., "Nonlinear regression with censored data" 49 : 34-44, 2005

      12 Zhou, M., "M-estimation in censored linear models" 79 : 837-841, 1992

      13 Miller, R. G., "Least squares regression with censored data" 63 : 449-464, 1976

      14 Suykens, J. A. K., "Least square support vector machine classifier" 9 : 293-300, 1999

      15 심주용, "Kernel method for autoregressive data" 한국데이터정보과학회 20 (20): 949-954, 2009

      16 Mercer, J., "Functions of positive and negative type and their connection with theory of integral equations" 415-446, 1909

      17 석경하, "Doubly penalized kernel method for heteroscedastic autoregressive data" 한국데이터정보과학회 21 (21): 155-162, 2010

      18 Stute, W., "Consistent estimation under random censorship when covariables are available" 45 : 89-103, 1993

      19 Yang, S., "Censored median regression using weighted empirical survival and hazard functions" 94 : 137-145, 1999

      20 심주용, "Censored Kernel Ridge Regression" 한국데이터정보과학회 16 (16): 1045-1052, 2005

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2022 평가예정 계속평가 신청대상 (등재유지)
      2017-01-01 평가 우수등재학술지 선정 (계속평가)
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 유지 (등재후보2차) KCI등재후보
      2002-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2001-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.18 1.18 1.07
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      1.01 0.91 0.911 0.35
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