Regularization perspectives on support vector machines (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Regularization perspectives on support vector machines" in English language version.

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6doi.org

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3semanticscholar.org

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3mit.edu

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1nih.gov

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doi.org

Cortes, Corinna; Vladimir Vapnik (1995). "Support-Vector Networks". Machine Learning. 20 (3): 273–297. doi:10.1007/BF00994018.
Lee, Yoonkyung; Wahba, Grace (2012). "Multicategory Support Vector Machines". Journal of the American Statistical Association. 99 (465): 67–81. CiteSeerX 10.1.1.22.1879. doi:10.1198/016214504000000098. S2CID 261035640.
Rosasco L.; De Vito E.; Caponnetto A.; Piana M.; Verri A. (May 2004). "Are Loss Functions All the Same". Neural Computation. 5. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. PMID 15070510. S2CID 11845688.
For insight on choosing the parameter, see, e.g., Wahba, Grace; Yonghua Wang (1990). "When is the optimal regularization parameter insensitive to the choice of the loss function". Communications in Statistics – Theory and Methods. 19 (5): 1685–1700. doi:10.1080/03610929008830285.
Schölkopf, Bernhard; Herbrich, Ralf; Smola, Alexander J. (2001). "A generalized representer theorem". In Helmbold, David P.; Williamson, Robert C. (eds.). Computational Learning Theory, 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16–19, 2001, Proceedings. Lecture Notes in Computer Science. Vol. 2111. Springer. pp. 416–426. doi:10.1007/3-540-44581-1_27. ISBN 978-3-540-42343-0.
Lin, Yi (July 2002). "Support Vector Machines and the Bayes Rule in Classification" (PDF). Data Mining and Knowledge Discovery. 6 (3): 259–275. doi:10.1023/A:1015469627679. S2CID 24759201.

ensmp.fr

cbio.ensmp.fr

Lin, Yi (July 2002). "Support Vector Machines and the Bayes Rule in Classification" (PDF). Data Mining and Knowledge Discovery. 6 (3): 259–275. doi:10.1023/A:1015469627679. S2CID 24759201.

mit.edu

web.mit.edu

Rifkin, Ryan (2002). Everything Old is New Again: A Fresh Look at Historical Approaches in Machine Learning (PDF). MIT (PhD thesis).
For a detailed derivation, see Rifkin, Ryan (2002). Everything Old is New Again: A Fresh Look at Historical Approaches in Machine Learning (PDF). MIT (PhD thesis).

mit.edu

Rosasco, Lorenzo. "Regularized Least-Squares and Support Vector Machines" (PDF).

nih.gov

pubmed.ncbi.nlm.nih.gov

Rosasco L.; De Vito E.; Caponnetto A.; Piana M.; Verri A. (May 2004). "Are Loss Functions All the Same". Neural Computation. 5. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. PMID 15070510. S2CID 11845688.

psu.edu

citeseerx.ist.psu.edu

Lee, Yoonkyung; Wahba, Grace (2012). "Multicategory Support Vector Machines". Journal of the American Statistical Association. 99 (465): 67–81. CiteSeerX 10.1.1.22.1879. doi:10.1198/016214504000000098. S2CID 261035640.
Rosasco L.; De Vito E.; Caponnetto A.; Piana M.; Verri A. (May 2004). "Are Loss Functions All the Same". Neural Computation. 5. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. PMID 15070510. S2CID 11845688.

semanticscholar.org

api.semanticscholar.org

Lee, Yoonkyung; Wahba, Grace (2012). "Multicategory Support Vector Machines". Journal of the American Statistical Association. 99 (465): 67–81. CiteSeerX 10.1.1.22.1879. doi:10.1198/016214504000000098. S2CID 261035640.
Rosasco L.; De Vito E.; Caponnetto A.; Piana M.; Verri A. (May 2004). "Are Loss Functions All the Same". Neural Computation. 5. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. PMID 15070510. S2CID 11845688.
Lin, Yi (July 2002). "Support Vector Machines and the Bayes Rule in Classification" (PDF). Data Mining and Knowledge Discovery. 6 (3): 259–275. doi:10.1023/A:1015469627679. S2CID 24759201.