吉洪诺夫正则化 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "吉洪诺夫正则化" in Chinese language version.

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archive.org

Hoerl, Arthur E.; Kennard, Robert W. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics. 1970, 12 (1): 55–67. JSTOR 1267351. doi:10.2307/1267351.
Hoerl, Arthur E.; Kennard, Robert W. Ridge Regression: Applications to Nonorthogonal Problems. Technometrics. 1970, 12 (1): 69–82. JSTOR 1267352. doi:10.2307/1267352.
Tikhonov, A. N.; V. Y. Arsenin. Solution of Ill-posed Problems. Washington: Winston & Sons. 1977. ISBN 0-470-99124-0.
Hoerl, Arthur E. Application of Ridge Analysis to Regression Problems. Chemical Engineering Progress. 1962, 58 (3): 54–59.
Hoerl, A. E.; R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics. 1970, 12 (1): 55–67. doi:10.1080/00401706.1970.10488634.
Wahba, G. Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics (Society for Industrial and Applied Mathematics). 1990. Bibcode:1990smod.conf.....W.
Amemiya, Takeshi. Advanced Econometrics. Harvard University Press. 1985: 60–61. ISBN 0-674-00560-0.

arxiv.org

van Wieringen, Wessel. Lecture notes on ridge regression. 2021-05-31. arXiv:1509.09169  [stat.ME].

biodiversitylibrary.org

Hilt, Donald E.; Seegrist, Donald W. Ridge, a computer program for calculating ridge regression estimates. 1977 [2023-09-24]. doi:10.5962/bhl.title.68934. （原始内容存档于2023-02-10）. ^{[页码请求]}

books.google.com

Gruber, Marvin. Improving Efficiency by Shrinkage: The James--Stein and Ridge Regression Estimators. CRC Press. 1998: 2 [2023-09-24]. ISBN 978-0-8247-0156-7. （原始内容存档于2022-05-10）.
Kennedy, Peter. A Guide to Econometrics Fifth. Cambridge: The MIT Press. 2003: 205–206. ISBN 0-262-61183-X.
Gruber, Marvin. Improving Efficiency by Shrinkage: The James–Stein and Ridge Regression Estimators. Boca Raton: CRC Press. 1998: 7–15. ISBN 0-8247-0156-9.
Beck, James Vere; Arnold, Kenneth J. Parameter Estimation in Engineering and Science. James Beck. 1977: 287 [2023-09-24]. ISBN 978-0-471-06118-2. （原始内容存档于2022-04-26）.
Jolliffe, I. T. Principal Component Analysis. Springer Science & Business Media. 2006: 178 [2023-09-24]. ISBN 978-0-387-22440-4. （原始内容存档于2022-04-18）.

doi.org

Hilt, Donald E.; Seegrist, Donald W. Ridge, a computer program for calculating ridge regression estimates. 1977 [2023-09-24]. doi:10.5962/bhl.title.68934. （原始内容存档于2023-02-10）. ^{[页码请求]}
Hoerl, Arthur E.; Kennard, Robert W. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics. 1970, 12 (1): 55–67. JSTOR 1267351. doi:10.2307/1267351.
Hoerl, Arthur E.; Kennard, Robert W. Ridge Regression: Applications to Nonorthogonal Problems. Technometrics. 1970, 12 (1): 69–82. JSTOR 1267352. doi:10.2307/1267352.
关于实践中 $\lambda$ 的选择，参Khalaf, Ghadban; Shukur, Ghazi. Choosing Ridge Parameter for Regression Problems. Communications in Statistics – Theory and Methods. 2005, 34 (5): 1177–1182. S2CID 122983724. doi:10.1081/STA-200056836.
Phillips, D. L. A Technique for the Numerical Solution of Certain Integral Equations of the First Kind. Journal of the ACM. 1962, 9: 84–97. S2CID 35368397. doi:10.1145/321105.321114.
Foster, M. An Application of the Wiener-Kolmogorov Smoothing Theory to Matrix Inversion. Journal of the Society for Industrial and Applied Mathematics. 1961, 9 (3): 387–392. doi:10.1137/0109031.
Hoerl, A. E.; R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics. 1970, 12 (1): 55–67. doi:10.1080/00401706.1970.10488634.
Guan, Naiyang; Tao, Dacheng; Luo, Zhigang; Yuan, Bo. Online nonnegative matrix factorization with robust stochastic approximation. IEEE Transactions on Neural Networks and Learning Systems. 2012, 23 (7): 1087–1099. PMID 24807135. S2CID 8755408. doi:10.1109/TNNLS.2012.2197827.
Golub, G.; Heath, M.; Wahba, G. Generalized cross-validation as a method for choosing a good ridge parameter (PDF). Technometrics. 1979, 21 (2): 215–223 [2023-09-24]. doi:10.1080/00401706.1979.10489751. （原始内容存档 (PDF)于2017-12-15）.

harvard.edu

ui.adsabs.harvard.edu

Wahba, G. Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics (Society for Industrial and Applied Mathematics). 1990. Bibcode:1990smod.conf.....W.

icml.cc

Ng, Andrew Y. Feature selection, L1 vs. L2 regularization, and rotational invariance (PDF). Proc. ICML. 2004 [2023-09-24]. （原始内容存档 (PDF)于2023-03-15）.

jstor.org

Hoerl, Arthur E.; Kennard, Robert W. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics. 1970, 12 (1): 55–67. JSTOR 1267351. doi:10.2307/1267351.
Hoerl, Arthur E.; Kennard, Robert W. Ridge Regression: Applications to Nonorthogonal Problems. Technometrics. 1970, 12 (1): 69–82. JSTOR 1267352. doi:10.2307/1267352.

jussieu.fr

ipgp.jussieu.fr

Tarantola, Albert. Inverse Problem Theory and Methods for Model Parameter Estimation 1st. Philadelphia: Society for Industrial and Applied Mathematics (SIAM). 2005 [2018-08-09]. ISBN 0898717922. （原始内容存档于2021-02-25）.

nih.gov

ncbi.nlm.nih.gov

Guan, Naiyang; Tao, Dacheng; Luo, Zhigang; Yuan, Bo. Online nonnegative matrix factorization with robust stochastic approximation. IEEE Transactions on Neural Networks and Learning Systems. 2012, 23 (7): 1087–1099. PMID 24807135. S2CID 8755408. doi:10.1109/TNNLS.2012.2197827.

nsc.ru

a-server.math.nsc.ru

Tikhonov, Andrey Nikolayevich. Об устойчивости обратных задач [On the stability of inverse problems]. Doklady Akademii Nauk SSSR. 1943, 39 (5): 195–198. （原始内容存档于2005-02-27）.

semanticscholar.org

api.semanticscholar.org

关于实践中 $\lambda$ 的选择，参Khalaf, Ghadban; Shukur, Ghazi. Choosing Ridge Parameter for Regression Problems. Communications in Statistics – Theory and Methods. 2005, 34 (5): 1177–1182. S2CID 122983724. doi:10.1081/STA-200056836.
Phillips, D. L. A Technique for the Numerical Solution of Certain Integral Equations of the First Kind. Journal of the ACM. 1962, 9: 84–97. S2CID 35368397. doi:10.1145/321105.321114.
Guan, Naiyang; Tao, Dacheng; Luo, Zhigang; Yuan, Bo. Online nonnegative matrix factorization with robust stochastic approximation. IEEE Transactions on Neural Networks and Learning Systems. 2012, 23 (7): 1087–1099. PMID 24807135. S2CID 8755408. doi:10.1109/TNNLS.2012.2197827.

sintef.no

P. C. Hansen, "The L-curve and its use in the numerical treatment of inverse problems", [1] （页面存档备份，存于互联网档案馆）

springer.com

Tikhonov, Andrey Nikolayevich; Goncharsky, A.; Stepanov, V. V.; Yagola, Anatolij Grigorevic. Numerical Methods for the Solution of Ill-Posed Problems. Netherlands: Springer Netherlands. 30 June 1995 [9 August 2018]. ISBN 079233583X. （原始内容存档于2021-06-20）.
Tikhonov, Andrey Nikolaevich; Leonov, Aleksandr S.; Yagola, Anatolij Grigorevic. Nonlinear ill-posed problems. London: Chapman & Hall. 1998 [9 August 2018]. ISBN 0412786605. （原始内容存档于2021-06-15）.

web.archive.org

Hilt, Donald E.; Seegrist, Donald W. Ridge, a computer program for calculating ridge regression estimates. 1977 [2023-09-24]. doi:10.5962/bhl.title.68934. （原始内容存档于2023-02-10）. ^{[页码请求]}
Gruber, Marvin. Improving Efficiency by Shrinkage: The James--Stein and Ridge Regression Estimators. CRC Press. 1998: 2 [2023-09-24]. ISBN 978-0-8247-0156-7. （原始内容存档于2022-05-10）.
Beck, James Vere; Arnold, Kenneth J. Parameter Estimation in Engineering and Science. James Beck. 1977: 287 [2023-09-24]. ISBN 978-0-471-06118-2. （原始内容存档于2022-04-26）.
Jolliffe, I. T. Principal Component Analysis. Springer Science & Business Media. 2006: 178 [2023-09-24]. ISBN 978-0-387-22440-4. （原始内容存档于2022-04-18）.
Tikhonov, Andrey Nikolayevich. Об устойчивости обратных задач [On the stability of inverse problems]. Doklady Akademii Nauk SSSR. 1943, 39 (5): 195–198. （原始内容存档于2005-02-27）.
Tikhonov, Andrey Nikolayevich; Goncharsky, A.; Stepanov, V. V.; Yagola, Anatolij Grigorevic. Numerical Methods for the Solution of Ill-Posed Problems. Netherlands: Springer Netherlands. 30 June 1995 [9 August 2018]. ISBN 079233583X. （原始内容存档于2021-06-20）.
Tikhonov, Andrey Nikolaevich; Leonov, Aleksandr S.; Yagola, Anatolij Grigorevic. Nonlinear ill-posed problems. London: Chapman & Hall. 1998 [9 August 2018]. ISBN 0412786605. （原始内容存档于2021-06-15）.
Ng, Andrew Y. Feature selection, L1 vs. L2 regularization, and rotational invariance (PDF). Proc. ICML. 2004 [2023-09-24]. （原始内容存档 (PDF)于2023-03-15）.
P. C. Hansen, "The L-curve and its use in the numerical treatment of inverse problems", [1] （页面存档备份，存于互联网档案馆）
Golub, G.; Heath, M.; Wahba, G. Generalized cross-validation as a method for choosing a good ridge parameter (PDF). Technometrics. 1979, 21 (2): 215–223 [2023-09-24]. doi:10.1080/00401706.1979.10489751. （原始内容存档 (PDF)于2017-12-15）.
Tarantola, Albert. Inverse Problem Theory and Methods for Model Parameter Estimation 1st. Philadelphia: Society for Industrial and Applied Mathematics (SIAM). 2005 [2018-08-09]. ISBN 0898717922. （原始内容存档于2021-02-25）.

wisc.edu

stat.wisc.edu

Golub, G.; Heath, M.; Wahba, G. Generalized cross-validation as a method for choosing a good ridge parameter (PDF). Technometrics. 1979, 21 (2): 215–223 [2023-09-24]. doi:10.1080/00401706.1979.10489751. （原始内容存档 (PDF)于2017-12-15）.