Regularization (mathematics) (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Regularization (mathematics)" in English language version.

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mathscinet.ams.org

Tibshirani, Robert (1996). "Regression Shrinkage and Selection via the Lasso" (PostScript). Journal of the Royal Statistical Society, Series B. 58 (1): 267–288. doi:10.1111/j.2517-6161.1996.tb02080.x. MR 1379242. Retrieved 2009-03-19.
Candes, Emmanuel; Tao, Terence (2007). "The Dantzig selector: Statistical estimation when p is much larger than n". Annals of Statistics. 35 (6): 2313–2351. arXiv:math/0506081. doi:10.1214/009053606000001523. MR 2382644. S2CID 88524200.

archive.org

Bühlmann, Peter; Van De Geer, Sara (2011). Statistics for High-Dimensional Data. Springer Series in Statistics. p. 9. doi:10.1007/978-3-642-20192-9. ISBN 978-3-642-20191-2. If p > n, the ordinary least squares estimator is not unique and will heavily overfit the data. Thus, a form of complexity regularization will be necessary.

arxiv.org

Candes, Emmanuel; Tao, Terence (2007). "The Dantzig selector: Statistical estimation when p is much larger than n". Annals of Statistics. 35 (6): 2313–2351. arXiv:math/0506081. doi:10.1214/009053606000001523. MR 2382644. S2CID 88524200.
Małgorzata Bogdan; Ewout van den Berg; Weijie Su; Emmanuel J. Candes (2013). "Statistical estimation and testing via the ordered L1 norm". arXiv:1310.1969 [stat.ME].

cornell.edu

cs.cornell.edu

For the connection between maximum a posteriori estimation and ridge regression, see Weinberger, Kilian (July 11, 2018). "Linear / Ridge Regression". CS4780 Machine Learning Lecture 13. Cornell.

deeplearning.ai

Guo, Jingru. "AI Notes: Regularizing neural networks". deeplearning.ai. Retrieved 2024-02-04.

deeplearningbook.org

Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. Deep Learning Book. Retrieved 2021-01-29.

doi.org

Kratsios, Anastasis (2020). "Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization Data". Risks. 8 (2): [1]. doi:10.3390/risks8020040. hdl:20.500.11850/456375. Term structure models can be regularized to remove arbitrage opportunities [sic?].
Bühlmann, Peter; Van De Geer, Sara (2011). Statistics for High-Dimensional Data. Springer Series in Statistics. p. 9. doi:10.1007/978-3-642-20192-9. ISBN 978-3-642-20191-2. If p > n, the ordinary least squares estimator is not unique and will heavily overfit the data. Thus, a form of complexity regularization will be necessary.
Natarajan, B. (1995-04-01). "Sparse Approximate Solutions to Linear Systems". SIAM Journal on Computing. 24 (2): 227–234. doi:10.1137/S0097539792240406. ISSN 0097-5397. S2CID 2072045.
Tibshirani, Robert (1996). "Regression Shrinkage and Selection via the Lasso" (PostScript). Journal of the Royal Statistical Society, Series B. 58 (1): 267–288. doi:10.1111/j.2517-6161.1996.tb02080.x. MR 1379242. Retrieved 2009-03-19.
Arthur E. Hoerl; Robert W. Kennard (1970). "Ridge regression: Biased estimation for nonorthogonal problems". Technometrics. 12 (1): 55–67. doi:10.2307/1267351. JSTOR 1267351.
Li Wang; Michael D. Gordon; Ji Zhu (2006). "Regularized Least Absolute Deviations Regression and an Efficient Algorithm for Parameter Tuning". Sixth International Conference on Data Mining. pp. 690–700. doi:10.1109/ICDM.2006.134. ISBN 978-0-7695-2701-7.
Candes, Emmanuel; Tao, Terence (2007). "The Dantzig selector: Statistical estimation when p is much larger than n". Annals of Statistics. 35 (6): 2313–2351. arXiv:math/0506081. doi:10.1214/009053606000001523. MR 2382644. S2CID 88524200.

handle.net

hdl.handle.net

Kratsios, Anastasis (2020). "Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization Data". Risks. 8 (2): [1]. doi:10.3390/risks8020040. hdl:20.500.11850/456375. Term structure models can be regularized to remove arbitrage opportunities [sic?].

jstor.org

Arthur E. Hoerl; Robert W. Kennard (1970). "Ridge regression: Biased estimation for nonorthogonal problems". Technometrics. 12 (1): 55–67. doi:10.2307/1267351. JSTOR 1267351.

mdpi.com

Kratsios, Anastasis (2020). "Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization Data". Risks. 8 (2): [1]. doi:10.3390/risks8020040. hdl:20.500.11850/456375. Term structure models can be regularized to remove arbitrage opportunities [sic?].

semanticscholar.org

api.semanticscholar.org

Natarajan, B. (1995-04-01). "Sparse Approximate Solutions to Linear Systems". SIAM Journal on Computing. 24 (2): 227–234. doi:10.1137/S0097539792240406. ISSN 0097-5397. S2CID 2072045.
Candes, Emmanuel; Tao, Terence (2007). "The Dantzig selector: Statistical estimation when p is much larger than n". Annals of Statistics. 35 (6): 2313–2351. arXiv:math/0506081. doi:10.1214/009053606000001523. MR 2382644. S2CID 88524200.

siam.org

epubs.siam.org

Natarajan, B. (1995-04-01). "Sparse Approximate Solutions to Linear Systems". SIAM Journal on Computing. 24 (2): 227–234. doi:10.1137/S0097539792240406. ISSN 0097-5397. S2CID 2072045.

stanford.edu

www-stat.stanford.edu

Tibshirani, Robert (1996). "Regression Shrinkage and Selection via the Lasso" (PostScript). Journal of the Royal Statistical Society, Series B. 58 (1): 267–288. doi:10.1111/j.2517-6161.1996.tb02080.x. MR 1379242. Retrieved 2009-03-19.

worldcat.org

search.worldcat.org

Natarajan, B. (1995-04-01). "Sparse Approximate Solutions to Linear Systems". SIAM Journal on Computing. 24 (2): 227–234. doi:10.1137/S0097539792240406. ISSN 0097-5397. S2CID 2072045.