Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 660

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Problem formulations and solvers in linear SVM: a review

Chauhan, Vinod Kumar, Dahiya, Kalpana, Sharma, Anuj·2019·Artificial Intelligence Review
DOI 10.1007/s10462-018-9614-6
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10462-018-9614-6

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Research works printed but not linked: 124

  1. Abe S (2015) Fuzzy support vector machines for multilabel classification. Pattern Recogn 48(6):2110–2117 · doi:10.1016/j.patcog.2015.01.009
  2. Abe S, Inoue T (2002) Fuzzy support vector machines for multiclass problems. In: European symposium on artificial neural networks, pp 113–118
  3. Beck A, Tetruashvili L (2013) On the convergence of block coordinate descent type methods. SIAM J Optim 23(4):2037–2060 · DOI · Paper page
  4. Becker S, Fadili M (2012) A quasi-newton proximal splitting method. In: Advances in neural information processing systems 25: 26th annual conference on neural information processing systems 2012. Proceedings of a meeting held December 3–6, 2012, Lake Tahoe, Nevada, pp 2627–2635
  5. Blondel M, Seki K, Uehara K (2013) Block coordinate descent algorithms for large-scale sparse multiclass classification. Mach Learn 93(1):31–52 · DOI
  6. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual ACM workshop on computational learning theory, ACM Press, pp 144–152 · DOI · Paper page
  7. Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Lechevallier Y, Saporta G (eds) Proceedings of the 19th international conference on computational statistics (COMPSTAT’2010), Springer, Paris, pp 177–187. http://leon.bottou.org/papers/bottou-2010 · DOI · Paper page
  8. Bottou L (2012) Neural networks: tricks of the trade. 2nd edn, Springer, Berlin, pp 421–436
  9. Bottou L, Lin CJ (2007) Support vector machine solvers. Science 3(1):1–27
  10. Bottou L, Curtis FE, Nocedal J (2016) Optimization methods for large-scale machine learning. arXiv:1606.04838 · arXiv
  11. Bredensteiner EJ, Bennett KP (1999) Computational optimization: a tribute to olvi mangasarian, vol I, Springer, Boston, chap Multicategory Classification by Support Vector Machines, pp 53–79 · DOI
  12. Byrd RH, Hansen SL, Nocedal J, Singer Y (2016) A stochastic quasi-newton method for large-scale optimization. SIAM J Optim 26(2):1008–1031 · DOI · Paper page
  13. Cao LJ, Keerthi SS, Ong CJ, Zhang JQ, Periyathamby U, Fu XJ, Lee HP (2006) Parallel sequential minimal optimization for the training of support vector machines. IEEE Trans Neural Netw 17(4):1039–1049 · DOI · Paper page
  14. Cauchy AL (1847) Méthode générale pour la résolution des systèmes d’équations simultanées. Compte Rendu des S’eances de L’Acad’emie des Sciences XXV S’erie A(25):536–538
  15. Chang CC, Hsu CW, Lin CJ (2000) The analysis of decomposition methods for support vector machines. IEEE Transactions on Neural Networks 11(4):1003–1008 · DOI
  16. Chang KW, Hsieh CJ, Lin CJ (2008) Coordinate descent method for large-scale l2-loss linear support vector machines. J Mach Learn Res 9:1369–1398
  17. Chapelle O (2007) Training a support vector machine in the primal. Neural Comput 19(5):1155–1178 · DOI · Paper page
  18. Chauhan VK, Dahiya K, Sharma A (2017a) Mini-batch block-coordinate based stochastic average adjusted gradient methods to solve big data problems. In: Zhang ML, Noh YK (eds) Proceedings of the ninth Asian conference on machine learning, PMLR, Proceedings of machine learning research, vol 77, pp 49–64. http://proceedings.mlr.press/v77/chauhan17a.html
  19. Chauhan VK, Dahiya K, Sharma A (2017b) Trust region levenberg-marquardt method for linear svm. In: Ninth international conference on advances in pattern recognition (ICAPR-2017) · DOI
  20. Yc C, Su C (2016) Distance-based margin support vector machine for classification. Appl Math Comput 283:141–152
  21. Collobert R, Bengio S, Bengio Y (2002) A parallel mixture of svms for very large scale problems. Neural Comput 14(5):1105–1114 · DOI · Paper page
  22. Cotter A, Shalev-Shwartz S, Srebro N (2013) Learning optimally sparse support vector machines. In: Proceedings of the 30th ICML, pp 266–274
  23. Crammer K, Singer Y (2002) On the learnability and design of output codes for multiclass problems. Mach Learn 47(1995):201–233 · DOI · Paper page
  24. De S, Taylor G, Goldstein T (2015) Variance reduction for distributed stochastic gradient descent. arXiv:1512.01708 · arXiv
  25. Defazio A, Bach F, Lacoste-Julien S (2014) Saga: a fast incremental gradient method with support for non-strongly convex composite objectives. In: Proceedings of the 27th international conference on neural information processing systems, MIT Press, Cambridge, NIPS’14, pp 1646–1654
  26. Evgeniou T, Pontil M (2004) Regularized multi–task learning. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD ’04, pp 109–117 · DOI · Paper page
  27. Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines. In: Advances in computational mathematics, MIT Press, Cambridge, pp 1–50 · DOI
  28. Fan R, Chang K, Hsieh C, Wang X, Lin C (2008) LIBLINEAR: a library for large linear classification. JMLR 9:1871–1874
  29. Farquhar JDR, Meng H, Szedmak S, Hardoon DR, Shawe-Taylor J (2006) Two view learning: Svm-2k, theory and practice. In: Advances in neural information processing systems, MIT Press, Cambridge
  30. Ferris MC, Munson TS (2002) Interior-point methods for massive support vector machines. SIAM J Optim 13(3):783–804 · DOI · Paper page
  31. Fletcher R, Reeves CM (1964) Function minimization by conjugate gradients. Comput J 7(2):149–154. https://doi.org/10.1093/comjnl/7.2.149 · DOI · Paper page
  32. Franc V, Sonnenburg S (2008) Optimized cutting plane algorithm for support vector machines. In: Proceedings of the 25th international conference on machine learning, ACM, New York, NY, ICML ’08, pp 320–327 · DOI
  33. Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, KDD ’01, pp 77–86 · DOI · Paper page
  34. Gao Y, Sun S (2010) An empirical evaluation of linear and nonlinear kernels for text classification using support vector machines. In: 2010 seventh international conference on fuzzy systems and knowledge discovery, vol 4, pp 1502–1505, https://doi.org/10.1109/FSKD.2010.5569327 · doi:10.1109/fskd.2010.5569327
  35. Graf HP, Cosatto E, Bottou L, Durdanovic I, Vapnik V (2005) Parallel support vector machines: the cascade SVM. In: Advances in neural information processing systems pp 521–528
  36. Guermeur Y (2002) Combining discriminant models with new multi-class svms. Pattern Anal Appl 5(2):168–179 · DOI
  37. Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand 49(6):409–436 · DOI · Paper page
  38. Hsieh CJ, Chang KW, Lin CJ, Keerthi SS, Sundararajan S (2008) A dual coordinate descent method for large-scale linear svm. In: Proceedings of the 25th international conference on machine learning, ACM, New York, NY, ICML ’08, pp 408–415 · DOI · Paper page
  39. Hsieh CJ, Si S, Dhillon I (2014) A divide-and-conquer solver for Kernel support vector machines. JMLR W&Cp 32(1):566–574
  40. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425. https://doi.org/10.1109/72.991427 · DOI · Paper page
  41. Jayadeva, Khemchandani R, Chandra S (2004) Fast and robust learning through fuzzy linear proximal support vector machines. Neurocomput 61(C):401–411 · DOI
  42. Jayadeva KR, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910 · DOI · Paper page
  43. Khemchandani R, Chandra S (2017) Twin support vector machines–models, extensions and applications, studies in computational intelligence, vol 659. Springer, Berlin
  44. Ji Y, Sun S (2011) Multitask multiclass support vector machines. In: 2011 IEEE 11th international conference on data mining workshops, pp 512–518 · doi:10.1109/icdmw.2011.126
  45. Ji Y, Sun S (2013) Multitask multiclass support vector machines: model and experiments. Pattern Recogn 46(3):914–924 · DOI
  46. Joachims T (1999) Transductive inference for text classification using support vector machines. In: Proceedings of the sixteenth international conference on machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, ICML ’99, pp 200–209
  47. Joachims T (2006) Training linear SVMs in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, KDD ’06, pp 217–226 · DOI · Paper page
  48. Joachims T, Finley T, Yu CNJ (2009) Cutting-plane training of structural svms. Mach Learn 77(1):27–59 · DOI · Paper page
  49. Johnson R, Zhang T (2013) Accelerating stochastic gradient descent using predictive variance reduction. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ (eds) Advances in neural information processing systems 26, Curran Associates, Inc., pp 315–323
  50. Kao W, Chung K, Sun C, Lin C (2004) Decomposition methods for linear support vector machines. Neural Comput 16:1689–1704 · DOI · Paper page
  51. Keerthi SS, DeCoste D (2005) A modified finite newton method for fast solution of large scale linear svms. JMLR 6:341–361
  52. Keerthi SS, Decoste D (2006) Building support vector machines with reduced classifier complexity. JMLR 7:1493–1515
  53. Keerthi SS, Sundararajan S, Chang KW, Hsieh CJ, Lin CJ (2008) A sequential dual method for large scale multi-class linear svms. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, KDD ’08, pp 408–416 · DOI
  54. Knerr S, Personnaz L, Dreyfus G (1990) Neurocomputing: algorithms, architectures and applications. Springer, Berlin, pp 41–50 · DOI · Paper page
  55. Konečný J, Richtárik P (2013) Semi-stochastic gradient descent methods, 1:19. arXiv:1312.1666 · arXiv
  56. Kressel UHG (1999) Advances in Kernel methods. MIT Press, Cambridge, MA, pp 255–268
  57. Ladicky L, Torr P (2011) Locally linear support vector machines. ICML, Stockholm, pp 985–992
  58. Lazar A (2010) A comparison of linear support vector machine algorithms on large non-sparse datasets. In: Proceedings–9th ICMLA · doi:10.1109/icmla.2010.137
  59. Lee CP, Lin CJ (2013) A study on l2-loss (squared hinge-loss) multi-class svm. Neural Comput 25:1302–1323 · DOI
  60. Lee CP, Roth D (2015) Distributed box-constrained quadratic optimization for dual linear SVM. In: Proceedings of the 32nd ICML, 37
  61. Lee YJ, Mangasarian O (2001) Ssvm: a smooth support vector machine for classification. Comput Optim Appl 20(1):5–22 · DOI · Paper page
  62. Levenberg K (1944) A method for the solution of certain problems in least squares. Quart Appl Math 2:164–168 · DOI · Paper page
  63. Li L, Li Y, Su H, Chu J (2006) Intelligent computing: international conference on intelligent computing, ICIC 2006, Kunming, China, August 16–19, 2006. Proceedings, Part I, Springer Berlin Heidelberg, Berlin, Heidelberg, chap Least Squares Support Vector Machines Based on Support Vector Degrees, pp 1275–1281
  64. Li M, Andersen DG, Park JW, Smola AJ, Ahmed A, Josifovski V, Long J, Shekita EJ, Su BY (2014a) Scaling distributed machine learning with the parameter server. In: Proceedings of the 11th USENIX conference on operating systems design and implementation, USENIX Association, Berkeley, CA, OSDI’14, pp 583–598
  65. Li M, Zhang T, Chen Y, Smola AJ (2014b) Efficient mini-batch training for stochastic optimization. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, KDD ’14, pp 661–670 · DOI · Paper page
  66. Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471 · DOI · Paper page
  67. Lin CJ (2001) On the convergence of the decomposition method for support vector machines. IEEE Trans Neural Netw 12(6):1288–1298 · DOI · Paper page
  68. Lin CJ, Weng RC, Keerthi SS (2008) Trust region newton method for logistic regression. JMLR 9:627–650
  69. Lin CY, Tsai CH, Lee CP, Lin CJ (2014) Large-scale logistic regression and linear support vector machines using spark. In: 2014 IEEE international conference on Big Data (Big Data) pp 519–528 · DOI
  70. Lin HT, Lin CJ, Weng RC (2007) A note on platt’s probabilistic outputs for support vector machines. Mach Learn 68(3):267–276 · DOI · Paper page
  71. Liu D, Shi Y, Tian Y, Huang X (2016) Ramp loss least squares support vector machine. J Comput Sci 14:61–68 · DOI
  72. Lu Z, Xiao L (2015) On the complexity analysis of randomized block-coordinate descent methods. Math Program 152(1):615–642 · DOI · Paper page
  73. Luss R, d’Aspremont A (2009) Support vector machine classification with indefinite kernels. Math Program Comput 1(2):97–118 · DOI
  74. Mangasarian OL (2006) Exact 1-norm support vector machines via unconstrained convex differentiable minimization. J Mach Learn Res 7:1517–1530
  75. Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. JMLR 1:161–177
  76. Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74 · DOI · Paper page
  77. Moré JJ (1978) Numerical analysis. In: Proceedings of the biennial conference held at Dundee, June 28–July 1, 1977, Springer, Berlin, pp 105–116
  78. Needell D, Srebro N, Ward R (2014) Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm. In: Proceedings of the 27th international conference on neural information processing systems, MIT Press, Cambridge, MA, NIPS’14, pp 1017–1025
  79. Nie F, Huang Y, Wang X, Huang H (2014) New primal SVM solver with linear computational cost for big data classifications. In: Proceedings of the 31st international conference on international conference on machine learning, Vol 32, JMLR.org, ICML’14, pp II–505–II–513
  80. Nie F, Wang X, Huang H (2017) Multiclass capped Lp-norm SVM for robust classifications. The 31st AAAI Conference on Artificial Intelligence (AAAI), San Francisco, USA · DOI
  81. Nocedal WS (1999) Numerical optimization. Springer, New York · DOI · Paper page
  82. Parlett B (1998) The symmetric eigenvalue problem. Society for Industrial and Applied Mathematics, Philadelphia, PA · DOI · Paper page
  83. Peng X (2010) A nu-twin support vector machine (nu-tsvm) classifier and its geometric algorithms. Inf Sci 180(20):3863–3875 · DOI
  84. Platt JC, Cristianini N, Shawe-Taylor J (2000) Large margin dags for multiclass classification. Adv Neural Inf Process Syst 12:547–553
  85. Prez-Cruz F, Weston J, Herrmann D, Schölkopf B (2003) Extension of the nu-SVM range for classification. Nato Sci Series Sub Series iii Comput Syst Sci 190:179–196
  86. Recht B, Re C, Wright S, Niu F (2011) Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ (eds) Advances in neural information processing systems 24, Curran Associates, Inc., pp 693–701
  87. Richtárik P, Takáč M (2014) Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math Program 144(1–2):1–38 · DOI · Paper page
  88. Richtárik P, Takáč M (2016) Parallel coordinate descent methods for big data optimization. Math Program 156(1):433–484 · DOI · Paper page
  89. Schölkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245 · DOI
  90. Shalev-Shwartz S, Singer Y (2006) Online learning meets optimization in the dual, pp 423–437 · DOI
  91. Shalev-Shwartz S, Zhang T (2013a) Accelerated mini-batch stochastic dual coordinate ascent. In: NIPS’ 13 Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol 1. pp 378–385
  92. Shalev-Shwartz S, Zhang T (2013b) Stochastic dual coordinate ascent methods for regularized loss. JMLR 14(1):567–599
  93. Shalev-Shwartz S, Singer Y, Srebro N (2007) Pegasos: primal estimated sub-gradient solver for SVM. In: Proceedings of the 24th international conference on machine learning, ACM, New York, NY, ICML ’07, pp 807–814 · DOI · Paper page
  94. Shawe-Taylor J, Sun S (2011) A review of optimization methodologies in support vector machines. Neurocomputing 74(17):3609–3618 · DOI · Paper page
  95. Shi Z, Liu R (2016) A better convergence analysis of the block coordinate descent method for large scale machine learning. arXiv:1608.04826 · arXiv
  96. Stefano M, Mikhail B (2011) Laplacian support vector machines trained in the primal. JMLR 12:1149–1184
  97. Steinwart I (2004) Sparseness of support vector machinessome asymptotically sharp bounds. Adv Neural Inf Process Syst 16:1069–1076
  98. Stempfel G, Ralaivola L (2009) Learning SVMs from sloppily labeled data. In: Proceedings of the 19th international conference on artificial neural networks: Part I, Springer, Berlin, ICANN ’09, pp 884–893 · DOI
  99. Sun S, Xie X (2016) Semisupervised support vector machines with tangent space intrinsic manifold regularization. IEEE Trans Neural Netw Learn Syst 27(9):1827–1839 · doi:10.1109/tnnls.2015.2461009
  100. Suykens J, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300 · DOI · Paper page
  101. Tak M, Bijral A, Richtrik P, Srebro N (2013) Mini-batch primal and dual methods for SVMs. In: 30th international conference on machine learning, Springer, Berlin, pp 537–552
  102. Teo CH, Smola A, Vishwanathan SV, Le QV (2007) A scalable modular convex solver for regularized risk minimization. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, KDD ’07, pp 727–736 · DOI · Paper page
  103. Teo CH, Vishwanthan S, Smola AJ, Le QV (2010) Bundle methods for regularized risk minimization. JMLR 11:311–365
  104. Tsai CH, Lin CJCY (2014) Incremental and decremental training for linear classification. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’14, pp 343–352
  105. Tseng P (2001) Convergence of a block coordinate descent method for nondifferentiable minimization. J Optim Theory Appl 109(3):475–494 · DOI · Paper page
  106. Tsochantaridis I, Hofmann T, Joachims T, Altun Y (2004) Support vector machine learning for interdependent and structured output spaces. In: Proceedings of the twenty-first international conference on machine learning, ACM, New York, NY, ICML ’04, p 104 · DOI · Paper page
  107. Vapnik VN (1998) Statistical learning theory, 1st edn. Wiley, New York
  108. Wang Z, Crammer K, Vucetic S (2012) Breaking the curse of kernelization : budgeted stochastic gradient descent for large-scale SVM training. JMLR 13(1):3103–3131
  109. Willoughby RA (1979) Solutions of ill-posed problems (a. n. tikhonov and v. y. arsenin). SIAM Rev 21(2):266–267 · doi:10.1137/1021044
  110. Woodsend K, Gondzio J (2009) Hybrid MPI/OpenMP parallel linear support vector machine training. JMLR 10:1937–1953
  111. Xiao L, Zhang T (2014) A proximal stochastic gradient method with progressive variance reduction. SIAM J Optim 24(4):2057–2075 · DOI · Paper page
  112. Xie X, Sun S (2015) Multi-view twin support vector machines. Intell Data Anal 19(4):701–712 · DOI
  113. Xu C, Tao D, Xu C (2013) A survey on multi-view learning, pp 1–59. arXiv:1304.5634v1 · arXiv
  114. Xu Y, Yin W (2013) A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM J Imaging Sci 6(3):1758–1789 · DOI · Paper page
  115. Xu Y, Yin W (2015) Block stochastic gradient iteration for convex and nonconvex optimization. SIAM J Optim 25(3):1686–1716 · DOI
  116. Yang Y, Chen J, Zhu J (2016) Distributing the stochastic gradient sampler for large-scale lda. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, KDD ’16, pp 1975–1984 · DOI
  117. Ye J, Xiong T (2007) SVM versus least squares SVM. In: JMLR W&P, pp 644–651
  118. Yuan GX, Chang KW, Hsieh CJ, Lin CJ (2010) A comparison of optimization methods and software for large-scale L1-regularized linear classification. JMLR 11:3183–3234
  119. Yuan GX, Ho CH, Lin CJ (2012) Recent advances of large-scale linear classification. Proc IEEE 100(9):2584–2603 · DOI · Paper page
  120. Zanghirati G, Zanni L (2003) A parallel solver for large quadratic programs in training support vector machines. Parallel Comput 29(4):535–551 · DOI
  121. Zanni L, Serafini T, Zanghirati G (2006) Parallel software for training large scale support vector machines on multiprocessor systems. JMLR 7:1467–1492
  122. Zhang A, Gu Q (2016) Accelerated stochastic block coordinate descent with optimal sampling. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, KDD ’16, pp 2035–2044 · DOI
  123. Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the twenty-first international conference on machine learning, ACM, New York, NY, ICML ’04 · DOI · Paper page
  124. Zhu J, Rosset S, Hastie T, Tibshirani R (2003) 1-norm support vector machines. In: Neural information processing systems, MIT Press, Cambridge, p 16

Cited by

See all 660

Metadata

Authors
Chauhan, V. K., Dahiya, K., & Sharma, A.
Venue
Artificial Intelligence Review
Publisher
Springer Science and Business Media LLC
Published
2018-01-16
Volume
52
Issue
2
Pages
803-855
ISSN
  • 0269-2821 (ISSN-L)
  • 1573-7462
DOI
10.1007/s10462-018-9614-6
OpenAlex
W2783380896
Cited by
660
References
7
FWCI
11.32
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Problem formulations and solvers in linear SVM: a review. https://app.cochat.ai/papers/10.1007/s10462-018-9614-6
The paper
Chauhan, V. K., Dahiya, K., & Sharma, A. (2019). Problem formulations and solvers in linear SVM: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-018-9614-6

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.