Support vector machine (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Support vector machine" in English language version.

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23doi.org

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aclweb.org

Pradhan, Sameer S.; et al. (2 May 2004). Shallow Semantic Parsing using Support Vector Machines. Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004. Association for Computational Linguistics. pp. 233–240.

approximateinference.org

Florian Wenzel; Matthäus Deutsch; Théo Galy-Fajou; Marius Kloft; ”Scalable Approximate Inference for the Bayesian Nonlinear Support Vector Machine”

aramislab.fr

Cuingnet, Rémi; Rosso, Charlotte; Chupin, Marie; Lehéricy, Stéphane; Dormont, Didier; Benali, Habib; Samson, Yves; Colliot, Olivier (2011). "Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome" (PDF). Medical Image Analysis. 15 (5): 729–737. doi:10.1016/j.media.2011.05.007. PMID 21752695. Archived from the original (PDF) on 2018-12-22. Retrieved 2018-01-08.

arxiv.org

A. Maity (2016). "Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features". arXiv:1608.00501 [cs.CV].
Dietterich, Thomas G.; Bakiri, Ghulum (1995). "Solving Multiclass Learning Problems via Error-Correcting Output Codes" (PDF). Journal of Artificial Intelligence Research. 2: 263–286. arXiv:cs/9501101. Bibcode:1995cs........1101D. doi:10.1613/jair.105. S2CID 47109072. Archived (PDF) from the original on 2013-05-09.
Wenzel, Florian; Galy-Fajou, Theo; Deutsch, Matthäus; Kloft, Marius (2017). "Bayesian Nonlinear Support Vector Machines for Big Data". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 10534. pp. 307–322. arXiv:1707.05532. Bibcode:2017arXiv170705532W. doi:10.1007/978-3-319-71249-9_19. ISBN 978-3-319-71248-2. S2CID 4018290.
Fennell, Peter; Zuo, Zhiya; Lerman, Kristina (2019-12-01). "Predicting and explaining behavioral data with structured feature space decomposition". EPJ Data Science. 8. arXiv:1810.09841. doi:10.1140/epjds/s13688-019-0201-0.

berkeley.edu

people.eecs.berkeley.edu

DeCoste, Dennis (2002). "Training Invariant Support Vector Machines" (PDF). Machine Learning. 46: 161–190. doi:10.1023/A:1012454411458. S2CID 85843.

ccdlab.org

Statnikov, Alexander; Hardin, Douglas; & Aliferis, Constantin; (2006); "Using SVM weight-based methods to identify causally relevant and non-causally relevant variables", Sign, 1, 4.

clopinet.com

Boser, Bernhard E.; Guyon, Isabelle M.; Vapnik, Vladimir N. (1992). "A training algorithm for optimal margin classifiers". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 144. CiteSeerX 10.1.1.21.3818. doi:10.1145/130385.130401. ISBN 978-0897914970. S2CID 207165665.

columbia.edu

www1.cs.columbia.edu

Joachims, Thorsten. Transductive Inference for Text Classification using Support Vector Machines (PDF). Proceedings of the 1999 International Conference on Machine Learning (ICML 1999). pp. 200–209.

cornell.edu

cs.cornell.edu

https://www.cs.cornell.edu/people/tj/publications/tsochantaridis_etal_04a.pdf

diku.dk

image.diku.dk

Cortes, Corinna; Vapnik, Vladimir (1995). "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID 206787478.

doi.org

Cortes, Corinna; Vapnik, Vladimir (1995). "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID 206787478.
Vapnik, Vladimir N. (1997). Gerstner, Wulfram; Germond, Alain; Hasler, Martin; Nicoud, Jean-Daniel (eds.). "The Support Vector method". Artificial Neural Networks — ICANN'97. Berlin, Heidelberg: Springer: 261–271. doi:10.1007/BFb0020166. ISBN 978-3-540-69620-9.
Boser, Bernhard E.; Guyon, Isabelle M.; Vapnik, Vladimir N. (1992). "A training algorithm for optimal margin classifiers". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 144. CiteSeerX 10.1.1.21.3818. doi:10.1145/130385.130401. ISBN 978-0897914970. S2CID 207165665.
Joachims, Thorsten (1998). "Text categorization with Support Vector Machines: Learning with many relevant features". Machine Learning: ECML-98. Lecture Notes in Computer Science. Vol. 1398. Springer. pp. 137–142. doi:10.1007/BFb0026683. ISBN 978-3-540-64417-0.
Barghout, Lauren (2015). "Spatial-Taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation" (PDF). Granular Computing and Decision-Making. Studies in Big Data. Vol. 10. pp. 285–318. doi:10.1007/978-3-319-16829-6_12. ISBN 978-3-319-16828-9. S2CID 4154772. Archived from the original (PDF) on 2018-01-08. Retrieved 2018-01-08.
DeCoste, Dennis (2002). "Training Invariant Support Vector Machines" (PDF). Machine Learning. 46: 161–190. doi:10.1023/A:1012454411458. S2CID 85843.
Maitra, D. S.; Bhattacharya, U.; Parui, S. K. (August 2015). "CNN based common approach to handwritten character recognition of multiple scripts". 2015 13th International Conference on Document Analysis and Recognition (ICDAR). pp. 1021–1025. doi:10.1109/ICDAR.2015.7333916. ISBN 978-1-4799-1805-8. S2CID 25739012.
Gaonkar, B.; Davatzikos, C. (2013). "Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification". NeuroImage. 78: 270–283. doi:10.1016/j.neuroimage.2013.03.066. PMC 3767485. PMID 23583748.
Cuingnet, Rémi; Rosso, Charlotte; Chupin, Marie; Lehéricy, Stéphane; Dormont, Didier; Benali, Habib; Samson, Yves; Colliot, Olivier (2011). "Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome" (PDF). Medical Image Analysis. 15 (5): 729–737. doi:10.1016/j.media.2011.05.007. PMID 21752695. Archived from the original (PDF) on 2018-12-22. Retrieved 2018-01-08.
Shalev-Shwartz, Shai; Singer, Yoram; Srebro, Nathan; Cotter, Andrew (2010-10-16). "Pegasos: primal estimated sub-gradient solver for SVM". Mathematical Programming. 127 (1): 3–30. CiteSeerX 10.1.1.161.9629. doi:10.1007/s10107-010-0420-4. ISSN 0025-5610. S2CID 53306004.
Hsieh, Cho-Jui; Chang, Kai-Wei; Lin, Chih-Jen; Keerthi, S. Sathiya; Sundararajan, S. (2008-01-01). "A dual coordinate descent method for large-scale linear SVM". Proceedings of the 25th international conference on Machine learning - ICML '08. New York, NY, USA: ACM. pp. 408–415. CiteSeerX 10.1.1.149.5594. doi:10.1145/1390156.1390208. ISBN 978-1-60558-205-4. S2CID 7880266.
Rosasco, Lorenzo; De Vito, Ernesto; Caponnetto, Andrea; Piana, Michele; Verri, Alessandro (2004-05-01). "Are Loss Functions All the Same?". Neural Computation. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. ISSN 0899-7667. PMID 15070510. S2CID 11845688.
Meyer, David; Leisch, Friedrich; Hornik, Kurt (September 2003). "The support vector machine under test". Neurocomputing. 55 (1–2): 169–186. doi:10.1016/S0925-2312(03)00431-4.
Duan, Kai-Bo; Keerthi, S. Sathiya (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study" (PDF). Multiple Classifier Systems. LNCS. Vol. 3541. pp. 278–285. CiteSeerX 10.1.1.110.6789. doi:10.1007/11494683_28. ISBN 978-3-540-26306-7. Archived from the original (PDF) on 2013-05-03. Retrieved 2019-07-18.
Hsu, Chih-Wei & Lin, Chih-Jen (2002). "A Comparison of Methods for Multiclass Support Vector Machines" (PDF). IEEE Transactions on Neural Networks. 13 (2): 415–25. doi:10.1109/72.991427. PMID 18244442. Archived from the original (PDF) on 2013-05-03. Retrieved 2018-01-08.
Dietterich, Thomas G.; Bakiri, Ghulum (1995). "Solving Multiclass Learning Problems via Error-Correcting Output Codes" (PDF). Journal of Artificial Intelligence Research. 2: 263–286. arXiv:cs/9501101. Bibcode:1995cs........1101D. doi:10.1613/jair.105. S2CID 47109072. Archived (PDF) from the original on 2013-05-09.
Lee, Yoonkyung; Lin, Yi; Wahba, Grace (2004). "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 7066611.
Smola, Alex J.; Schölkopf, Bernhard (2004). "A tutorial on support vector regression" (PDF). Statistics and Computing. 14 (3): 199–222. CiteSeerX 10.1.1.41.1452. doi:10.1023/B:STCO.0000035301.49549.88. S2CID 15475. Archived (PDF) from the original on 2012-01-31.
Polson, Nicholas G.; Scott, Steven L. (2011). "Data Augmentation for Support Vector Machines". Bayesian Analysis. 6 (1): 1–23. doi:10.1214/11-BA601.
Wenzel, Florian; Galy-Fajou, Theo; Deutsch, Matthäus; Kloft, Marius (2017). "Bayesian Nonlinear Support Vector Machines for Big Data". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 10534. pp. 307–322. arXiv:1707.05532. Bibcode:2017arXiv170705532W. doi:10.1007/978-3-319-71249-9_19. ISBN 978-3-319-71248-2. S2CID 4018290.
Ferris, Michael C.; Munson, Todd S. (2002). "Interior-Point Methods for Massive Support Vector Machines" (PDF). SIAM Journal on Optimization. 13 (3): 783–804. CiteSeerX 10.1.1.216.6893. doi:10.1137/S1052623400374379. S2CID 13563302. Archived (PDF) from the original on 2008-12-04.
Mohamad, Ismail; Usman, Dauda (2013-09-01). "Standardization and Its Effects on K-Means Clustering Algorithm". Research Journal of Applied Sciences, Engineering and Technology. 6 (17): 3299–3303. doi:10.19026/rjaset.6.3638.
Fennell, Peter; Zuo, Zhiya; Lerman, Kristina (2019-12-01). "Predicting and explaining behavioral data with structured feature space decomposition". EPJ Data Science. 8. arXiv:1810.09841. doi:10.1140/epjds/s13688-019-0201-0.

harvard.edu

ui.adsabs.harvard.edu

Dietterich, Thomas G.; Bakiri, Ghulum (1995). "Solving Multiclass Learning Problems via Error-Correcting Output Codes" (PDF). Journal of Artificial Intelligence Research. 2: 263–286. arXiv:cs/9501101. Bibcode:1995cs........1101D. doi:10.1613/jair.105. S2CID 47109072. Archived (PDF) from the original on 2013-05-09.
Wenzel, Florian; Galy-Fajou, Theo; Deutsch, Matthäus; Kloft, Marius (2017). "Bayesian Nonlinear Support Vector Machines for Big Data". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 10534. pp. 307–322. arXiv:1707.05532. Bibcode:2017arXiv170705532W. doi:10.1007/978-3-319-71249-9_19. ISBN 978-3-319-71248-2. S2CID 4018290.

iastate.edu

cs.iastate.edu

Duan, Kai-Bo; Keerthi, S. Sathiya (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study" (PDF). Multiple Classifier Systems. LNCS. Vol. 3541. pp. 278–285. CiteSeerX 10.1.1.110.6789. doi:10.1007/11494683_28. ISBN 978-3-540-26306-7. Archived from the original (PDF) on 2013-05-03. Retrieved 2019-07-18.
Hsu, Chih-Wei & Lin, Chih-Jen (2002). "A Comparison of Methods for Multiclass Support Vector Machines" (PDF). IEEE Transactions on Neural Networks. 13 (2): 415–25. doi:10.1109/72.991427. PMID 18244442. Archived from the original (PDF) on 2013-05-03. Retrieved 2018-01-08.

ieee.org

ieeexplore.ieee.org

Maitra, D. S.; Bhattacharya, U.; Parui, S. K. (August 2015). "CNN based common approach to handwritten character recognition of multiple scripts". 2015 13th International Conference on Document Analysis and Recognition (ICDAR). pp. 1021–1025. doi:10.1109/ICDAR.2015.7333916. ISBN 978-1-4799-1805-8. S2CID 25739012.
Rosasco, Lorenzo; De Vito, Ernesto; Caponnetto, Andrea; Piana, Michele; Verri, Alessandro (2004-05-01). "Are Loss Functions All the Same?". Neural Computation. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. ISSN 0899-7667. PMID 15070510. S2CID 11845688.

interfacesymposia.org

Lee, Yoonkyung; Lin, Yi & Wahba, Grace (2001). "Multicategory Support Vector Machines" (PDF). Computing Science and Statistics. 33. Archived (PDF) from the original on 2013-06-17.

jair.org

Dietterich, Thomas G.; Bakiri, Ghulum (1995). "Solving Multiclass Learning Problems via Error-Correcting Output Codes" (PDF). Journal of Artificial Intelligence Research. 2: 263–286. arXiv:cs/9501101. Bibcode:1995cs........1101D. doi:10.1613/jair.105. S2CID 47109072. Archived (PDF) from the original on 2013-05-09.

jmlr.org

Van den Burg, Gerrit J. J. & Groenen, Patrick J. F. (2016). "GenSVM: A Generalized Multiclass Support Vector Machine" (PDF). Journal of Machine Learning Research. 17 (224): 1–42.

kuleuven.be

lirias.kuleuven.be

Suykens, Johan A. K.; Vandewalle, Joos P. L.; "Least squares support vector machine classifiers", Neural Processing Letters, vol. 9, no. 3, Jun. 1999, pp. 293–300.

microsoft.com

research.microsoft.com

Platt, John C. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines (PDF). NIPS. Archived (PDF) from the original on 2015-07-02.

mit.edu

jmlr.csail.mit.edu

Crammer, Koby & Singer, Yoram (2001). "On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines" (PDF). Journal of Machine Learning Research. 2: 265–292. Archived (PDF) from the original on 2015-08-29.

people.csail.mit.edu

Allen Zhu, Zeyuan; Chen, Weizhu; Wang, Gang; Zhu, Chenguang; Chen, Zheng (2009). P-packSVM: Parallel Primal grAdient desCent Kernel SVM (PDF). ICDM. Archived (PDF) from the original on 2014-04-07.

nih.gov

pubmed.ncbi.nlm.nih.gov

Gaonkar, B.; Davatzikos, C. (2013). "Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification". NeuroImage. 78: 270–283. doi:10.1016/j.neuroimage.2013.03.066. PMC 3767485. PMID 23583748.
Cuingnet, Rémi; Rosso, Charlotte; Chupin, Marie; Lehéricy, Stéphane; Dormont, Didier; Benali, Habib; Samson, Yves; Colliot, Olivier (2011). "Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome" (PDF). Medical Image Analysis. 15 (5): 729–737. doi:10.1016/j.media.2011.05.007. PMID 21752695. Archived from the original (PDF) on 2018-12-22. Retrieved 2018-01-08.
Rosasco, Lorenzo; De Vito, Ernesto; Caponnetto, Andrea; Piana, Michele; Verri, Alessandro (2004-05-01). "Are Loss Functions All the Same?". Neural Computation. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. ISSN 0899-7667. PMID 15070510. S2CID 11845688.
Hsu, Chih-Wei & Lin, Chih-Jen (2002). "A Comparison of Methods for Multiclass Support Vector Machines" (PDF). IEEE Transactions on Neural Networks. 13 (2): 415–25. doi:10.1109/72.991427. PMID 18244442. Archived from the original (PDF) on 2013-05-03. Retrieved 2018-01-08.

ncbi.nlm.nih.gov

Gaonkar, B.; Davatzikos, C. (2013). "Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification". NeuroImage. 78: 270–283. doi:10.1016/j.neuroimage.2013.03.066. PMC 3767485. PMID 23583748.

nips.cc

papers.nips.cc

Jin, Chi; Wang, Liwei (2012). Dimensionality dependent PAC-Bayes margin bound. Advances in Neural Information Processing Systems. CiteSeerX 10.1.1.420.3487. Archived from the original on 2015-04-02.
Drucker, Harris; Burges, Christ. C.; Kaufman, Linda; Smola, Alexander J.; and Vapnik, Vladimir N. (1997); "Support Vector Regression Machines", in Advances in Neural Information Processing Systems 9, NIPS 1996, 155–161, MIT Press.

nrbook.com

apps.nrbook.com

Press, William H.; Teukolsky, Saul A.; Vetterling, William T.; Flannery, Brian P. (2007). "Section 16.5. Support Vector Machines". Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press. ISBN 978-0-521-88068-8. Archived from the original on 2011-08-11.

ntu.edu.tw

csie.ntu.edu.tw

Hsu, Chih-Wei; Chang, Chih-Chung & Lin, Chih-Jen (2003). A Practical Guide to Support Vector Classification (PDF) (Technical report). Department of Computer Science and Information Engineering, National Taiwan University. Archived (PDF) from the original on 2013-06-25.
Fan, Rong-En; Chang, Kai-Wei; Hsieh, Cho-Jui; Wang, Xiang-Rui; Lin, Chih-Jen (2008). "LIBLINEAR: A library for large linear classification" (PDF). Journal of Machine Learning Research. 9: 1871–1874.

pascal-network.org

eprints.pascal-network.org

Smola, Alex J.; Schölkopf, Bernhard (2004). "A tutorial on support vector regression" (PDF). Statistics and Computing. 14 (3): 199–222. CiteSeerX 10.1.1.41.1452. doi:10.1023/B:STCO.0000035301.49549.88. S2CID 15475. Archived (PDF) from the original on 2012-01-31.

psu.edu

citeseerx.ist.psu.edu

Cortes, Corinna; Vapnik, Vladimir (1995). "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID 206787478.
Boser, Bernhard E.; Guyon, Isabelle M.; Vapnik, Vladimir N. (1992). "A training algorithm for optimal margin classifiers". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 144. CiteSeerX 10.1.1.21.3818. doi:10.1145/130385.130401. ISBN 978-0897914970. S2CID 207165665.
Jin, Chi; Wang, Liwei (2012). Dimensionality dependent PAC-Bayes margin bound. Advances in Neural Information Processing Systems. CiteSeerX 10.1.1.420.3487. Archived from the original on 2015-04-02.
Shalev-Shwartz, Shai; Singer, Yoram; Srebro, Nathan; Cotter, Andrew (2010-10-16). "Pegasos: primal estimated sub-gradient solver for SVM". Mathematical Programming. 127 (1): 3–30. CiteSeerX 10.1.1.161.9629. doi:10.1007/s10107-010-0420-4. ISSN 0025-5610. S2CID 53306004.
Hsieh, Cho-Jui; Chang, Kai-Wei; Lin, Chih-Jen; Keerthi, S. Sathiya; Sundararajan, S. (2008-01-01). "A dual coordinate descent method for large-scale linear SVM". Proceedings of the 25th international conference on Machine learning - ICML '08. New York, NY, USA: ACM. pp. 408–415. CiteSeerX 10.1.1.149.5594. doi:10.1145/1390156.1390208. ISBN 978-1-60558-205-4. S2CID 7880266.
Rosasco, Lorenzo; De Vito, Ernesto; Caponnetto, Andrea; Piana, Michele; Verri, Alessandro (2004-05-01). "Are Loss Functions All the Same?". Neural Computation. 16 (5): 1063–1076. CiteSeerX 10.1.1.109.6786. doi:10.1162/089976604773135104. ISSN 0899-7667. PMID 15070510. S2CID 11845688.
Duan, Kai-Bo; Keerthi, S. Sathiya (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study" (PDF). Multiple Classifier Systems. LNCS. Vol. 3541. pp. 278–285. CiteSeerX 10.1.1.110.6789. doi:10.1007/11494683_28. ISBN 978-3-540-26306-7. Archived from the original (PDF) on 2013-05-03. Retrieved 2019-07-18.
Lee, Yoonkyung; Lin, Yi; Wahba, Grace (2004). "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 7066611.
Smola, Alex J.; Schölkopf, Bernhard (2004). "A tutorial on support vector regression" (PDF). Statistics and Computing. 14 (3): 199–222. CiteSeerX 10.1.1.41.1452. doi:10.1023/B:STCO.0000035301.49549.88. S2CID 15475. Archived (PDF) from the original on 2012-01-31.
Ferris, Michael C.; Munson, Todd S. (2002). "Interior-Point Methods for Massive Support Vector Machines" (PDF). SIAM Journal on Optimization. 13 (3): 783–804. CiteSeerX 10.1.1.216.6893. doi:10.1137/S1052623400374379. S2CID 13563302. Archived (PDF) from the original on 2008-12-04.

scikit-learn.org

"1.4. Support Vector Machines — scikit-learn 0.20.2 documentation". Archived from the original on 2017-11-08. Retrieved 2017-11-08.

semanticscholar.org

api.semanticscholar.org

Cortes, Corinna; Vapnik, Vladimir (1995). "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID 206787478.
Boser, Bernhard E.; Guyon, Isabelle M.; Vapnik, Vladimir N. (1992). "A training algorithm for optimal margin classifiers". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 144. CiteSeerX 10.1.1.21.3818. doi:10.1145/130385.130401. ISBN 978-0897914970. S2CID 207165665.
Barghout, Lauren (2015). "Spatial-Taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation" (PDF). Granular Computing and Decision-Making. Studies in Big Data. Vol. 10. pp. 285–318. doi:10.1007/978-3-319-16829-6_12. ISBN 978-3-319-16828-9. S2CID 4154772. Archived from the original (PDF) on 2018-01-08. Retrieved 2018-01-08.
DeCoste, Dennis (2002). "Training Invariant Support Vector Machines" (PDF). Machine Learning. 46: 161–190. doi:10.1023/A:1012454411458. S2CID 85843.
Maitra, D. S.; Bhattacharya, U.; Parui, S. K. (August 2015). "CNN based common approach to handwritten character recognition of multiple scripts". 2015 13th International Conference on Document Analysis and Recognition (ICDAR). pp. 1021–1025. doi:10.1109/ICDAR.2015.7333916. ISBN 978-1-4799-1805-8. S2CID 25739012.
Shalev-Shwartz, Shai; Singer, Yoram; Srebro, Nathan; Cotter, Andrew (2010-10-16). "Pegasos: primal estimated sub-gradient solver for SVM". Mathematical Programming. 127 (1): 3–30. CiteSeerX 10.1.1.161.9629. doi:10.1007/s10107-010-0420-4. ISSN 0025-5610. S2CID 53306004.
Hsieh, Cho-Jui; Chang, Kai-Wei; Lin, Chih-Jen; Keerthi, S. Sathiya; Sundararajan, S. (2008-01-01). "A dual coordinate descent method for large-scale linear SVM". Proceedings of the 25th international conference on Machine learning - ICML '08. New York, NY, USA: ACM. pp. 408–415. CiteSeerX 10.1.1.149.5594. doi:10.1145/1390156.1390208. ISBN 978-1-60558-205-4. S2CID 7880266.
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Shalev-Shwartz, Shai; Singer, Yoram; Srebro, Nathan; Cotter, Andrew (2010-10-16). "Pegasos: primal estimated sub-gradient solver for SVM". Mathematical Programming. 127 (1): 3–30. CiteSeerX 10.1.1.161.9629. doi:10.1007/s10107-010-0420-4. ISSN 0025-5610. S2CID 53306004.
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