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Top 100 SVM Publications

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  1. JOACHIMS, T., 1997. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Springer. [Cited by 2277] (216.02/year)
  2. CRISTIANINI, N. and J. SHAWE-TAYLOR, 2000. An introduction to Support Vector Machines. [Cited by 1509] (200.11/year)
  3. CORTES, C. and V. VAPNIK, 1995. Support-vector networks. Machine Learning. [Cited by 2683] (213.94/year)
  4. BURGES, C.J.C., 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. [Cited by 3793] (397.56/year)
  5. SCHOLKOPF, B. and A.J. SMOLA, 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press Cambridge, MA, USA. [Cited by 1824] (278.87/year)
  6. JOACHIMS, T., 1999. 11 Making Large-Scale Support Vector Machine Learning Practical. Advances in Kernel Methods: Support Vector Learning. [Cited by 1522] (178.20/year)
  7. BROWN, M.P.S., et al., 2000. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences. [Cited by 1134] (150.38/year)
  8. CHANG, C.C. and C.J. LIN, 2001. LIBSVM: a library for support vector machines. Software available at http://www. csie. ntu. edu. tw/cjlin/ …. [Cited by 1623] (248.14/year)
  9. PLATT, J.C., 1999. 12 Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Advances in Kernel Methods: Support Vector Learning. [Cited by 1442] (168.84/year)
  10. OSUNA, E., R. FREUND and F. GIROSI…, 1997. Training support vector machines: an application to face detection. Proceedings of the IEEE Conference on Computer Vision and …. [Cited by 1104] (104.74/year)
  11. CRISTIANINI, N. and J. SHAWE-TAYLOR, 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. books.google.com. [Cited by 2354] (312.17/year)
  12. FUREY, T.S., et al., 2000. Support vector machine classification and validation of cancer tissue samples using microarray …. Bioinformatics. [Cited by 703] (93.23/year)
  13. SUYKENS, J.A.K. and J. VANDEWALLE, 1999. Least Squares Support Vector Machine Classifiers. Neural Processing Letters. [Cited by 613] (71.77/year)
  14. HSU, C.W. and C.J. LIN, 2002. A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on. [Cited by 858] (154.85/year)
  15. OSUNA, E., R. FREUND and F. GIROSI, An improved training algorithm for support vector machines. ieeexplore.ieee.org. [Cited by 512] (?/year)
  16. JOACHIMS, T., 1999. Making large-scale support vector machine learning practical. Advances in kernel methods: support vector learning table of …. [Cited by 423] (49.53/year)
  17. JOACHIMS, T., 1999. Transductive inference for text classification using support vector machines. Proceedings of the Sixteenth International Conference on …. [Cited by 580] (67.91/year)
  18. SMOLA, A.J. and B. SCHöLKOPF, 2004. A tutorial on support vector regression. Statistics and Computing. [Cited by 811] (229.05/year)
  19. GUYON, I., et al., 2002. Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning. [Cited by 793] (143.12/year)
  20. PLATT, J., 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. [Cited by 559] (65.45/year)
  21. VAPNIK, V., S.E. GOLOWICH and A. SMOLA, 1997. Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. Advances in Neural Information Processing Systems 9. [Cited by 448] (42.50/year)
  22. WESTON, J. and C. WATKINS, 1999. Multi-class support vector machines. Proceedings ESANN, Brussels. [Cited by 380] (44.49/year)
  23. PLATT, J., 1999. Sequential minimal optimization: A fast algorithm for training support vector machines. Advances in Kernel Methods-Support Vector Learning. [Cited by 464] (54.33/year)
  24. GUNN, S.R., 1998. Support Vector Machines for Classification and Regression. ISIS Technical Report. [Cited by 518] (54.29/year)
  25. SCHöLKOPF, B., C.J.C. BURGES and A.J. SMOLA, 1999. Advances in Kernel Methods: Support Vector Learning. books.google.com. [Cited by 173] (20.26/year)
  26. SCHöLKOPF, B. and A.J. SMOLA, 2002. Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. books.google.com. [Cited by 786] (141.86/year)
  27. CHAPELLE, O., et al., 2002. Choosing Multiple Parameters for Support Vector Machines. Machine Learning. [Cited by 453] (81.76/year)
  28. SCHOLKOPF, B., et al., 2000. New Support Vector Algorithms. Neural Computation. [Cited by 452] (59.94/year)
  29. SUYKENS, J.A.K., 2002. Least Squares Support Vector Machines. books.google.com. [Cited by 519] (93.67/year)
  30. SCHOLKOPF, B., et al., 1997. Comparing support vector machines with Gaussian kernels to radialbasis function classifiers. Signal Processing, IEEE Transactions on [see also Acoustics, …. [Cited by 385] (36.52/year)
  31. SCHöLKOPF, B., 1997. Support Vector Learning. R. Oldenbourg Verlag. [Cited by 333] (31.59/year)
  32. SCHOLKOPF, B., C.J.C. BURGES and A.J. SMOLA…, 1999. Advances in Kernel Methods: Support Vector Learning. [Cited by 435] (50.93/year)
  33. DRUCKER, H., D. WU and V.N. VAPNIK, 1999. Support Vector Machines for Spam Categorization. IEEE TRANSACTIONS ON NEURAL NETWORKS. [Cited by 331] (38.76/year)
  34. COLLOBERT, R. and S. BENGIO, 2001. SVMTorch: Support Vector Machines for Large-Scale Regression Problems. Journal of Machine Learning Research. [Cited by 355] (54.27/year)
  35. EVGENIOU, T., M. PONTIL and T. POGGIO, 2000. 10 Regularization Networks and Support Vector Machines. Advances in Large-Margin Classifiers. [Cited by 417] (55.30/year)
  36. HUA, S. and Z. SUN, 2001. Support vector machine approach for protein subcellular localization prediction. Bioinformatics. [Cited by 329] (50.30/year)
  37. TONG, S. and E. CHANG, 2001. Support vector machine active learning for image retrieval. Proceedings of the ninth ACM international conference on …. [Cited by 353] (53.97/year)
  38. PONTIL, M. and A. VERRI, 1998. Support vector machines for 3 D object recognition. IEEE Transactions on Pattern Analysis and Machine …. [Cited by 327] (34.27/year)
  39. JOACHIMS, T., 2002. Learning to Classify Text Using Support Vector Machines. books.google.com. [Cited by 460] (83.02/year)
  40. CHAPELLE, O., P. HAFFNER and V.N. VAPNIK, 1999. Support vector machines for histogram-based image classification. Neural Networks, IEEE Transactions on. [Cited by 323] (37.82/year)
  41. OSUNA, E., R. FREUND and F. GIROSI, 1997. Support Vector Machines: Training and Applications. [Cited by 330] (31.31/year)
  42. MULLER, K.R., et al., 1997. Predicting time series with support vector machines. Proceedings of the International Conference on Artificial …. [Cited by 242] (22.96/year)
  43. BURGES, C.J.C. and B. SCHOLKOPF, 1997. Improving the Accuracy and Speed of Support Vector Machines. Advances in Neural Information Processing Systems 9. [Cited by 221] (20.97/year)
  44. DRUCKER, H., et al., 1997. Support Vector Regression Machines. Advances in Neural Information Processing Systems 9. [Cited by 228] (21.63/year)
  45. CAUWENBERGHS, G., et al., 2001. Incremental and Decremental Support Vector Machine Learning. Advances in Neural Information Processing Systems 13. [Cited by 200] (30.58/year)
  46. BEN-HUR, A., et al., 2001. Support vector clustering. Journal of Machine Learning Research. [Cited by 272] (41.59/year)
  47. TONG, S. and D. KOLLER, 2002. Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research. [Cited by 329] (59.38/year)
  48. BURGES, C.J.C., 1996. Simplified support vector decision rules. Proceedings of the 13th International Conference on Machine …. [Cited by 249] (21.58/year)
  49. GIROSI, F., 1998. An Equivalence Between Sparse Approximation And Support Vector Machines. Neural Computation. [Cited by 308] (32.28/year)
  50. SYED, N., H. LIU and K.K. SUNG, 1999. Incremental learning with support vector machines. … of the Workshop on Support Vector Machines at the …. [Cited by 127] (14.87/year)
  51. BRADLEY, P.S. and O.L. MANGASARIAN, 1998. Feature selection via concave minimization and support vector machines. Machine Learning Proceedings of the Fifteenth International …. [Cited by 212] (22.22/year)
  52. WESTON, J. and C. WATKINS, 1999. Support vector machines for multi-class pattern recognition. Proceedings of the Seventh European Symposium On Artificial …. [Cited by 170] (19.90/year)
  53. HSU, C.W., C.C. CHANG and C.J. LIN…, 2003. A practical guide to support vector classification. National Taiwan University, Tech. Rep., July. [Cited by 258] (56.82/year)
  54. BENNETT, K. and A. DEMIRIZ, 1998. Semi-supervised support vector machines. Advances in Neural Information Processing Systems. [Cited by 181] (18.97/year)
  55. KUDO, T. and Y. MATSUMOTO, 2001. Chunking with support vector machines. North American Chapter Of The Association For Computational …. [Cited by 233] (35.62/year)
  56. DING, C.H.Q. and I. DUBCHAK, 2001. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics. [Cited by 265] (40.52/year)
  57. CORTES, C. and V. VAPNIK, 1995. Support-vector network. Machine Learning. [Cited by 312] (24.88/year)
  58. ZIEN, A., et al., 2000. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics. [Cited by 235] (31.16/year)
  59. WAHBA, G., 1999. 6 Support Vector Machines, Reproducing Kernel Hubert Spaces, and Randomized GACV. Advances in Kernel Methods: Support Vector Learning. [Cited by 212] (24.82/year)
  60. SMOLA, A.J., B. SCHöLKOPF and K.R. MüLLER, 1998. The connection between regularization operators and support vector kernels. Neural Networks. [Cited by 222] (23.27/year)
  61. AMARI, S. and S. WU, 1999. Improving support vector machine classifiers by modifying kernel functions. Neural Networks. [Cited by 170] (19.90/year)
  62. TAX, D.M.J. and R.P.W. DUIN, 1999. Support vector domain description. Pattern Recognition Letters. [Cited by 201] (23.53/year)
  63. HEARST, M.A., et al., 1998. Support vector machines. IEEE Intelligent Systems. [Cited by 212] (22.22/year)
  64. HUA, S. and Z. SUN, 2001. … method of protein secondary structure prediction with high segment overlap measure: support vector …. Journal of Molecular Biology. [Cited by 213] (32.56/year)
  65. LEE, Y.J. and O.L. MANGASARIAN, 2001. RSVM: Reduced support vector machines. Proceedings of the First SIAM International Conference on …. [Cited by 199] (30.42/year)
  66. FRIE, T.T., N. CRISTIANINI and C. CAMPBELL, Proc. ICML. The kernel adatron algorithm: A fast and simple learning procedure for support vector machines.,". [Cited by 171] (?/year)
  67. SCHOLKOPF, B., C. BURGES and V. VAPNIK, 1996. Incorporating invariances in support vector learning machines. Artificial Neural Networks| ICANN. [Cited by 127] (11.00/year)
  68. SCHOHN, G. and D. COHN, 2000. Less is more: Active learning with support vector machines. Proceedings of the Seventeenth International Conference on …. [Cited by 160] (21.22/year)
  69. PLATT, J.C., 1999. … support vector machines using sequential minimal optimization, Advances in kernel methods: support …. [Cited by 181] (21.19/year)
  70. KREβEL…, U., 1999. Pairwise classification and support vector machines. Advances in Kernel Methods: Support Vector Learning. [Cited by 171] (20.02/year)
  71. MANGASARIAN, O.L. and D.R. MUSICANT, 1999. Successive overrelaxation for support vector machines. Neural Networks, IEEE Transactions on. [Cited by 164] (19.20/year)
  72. CHAPELLE, O. and V. VAPNIK, 1999. Model selection for support vector machines. Advances in Neural Information Processing Systems. [Cited by 135] (15.81/year)
  73. JI, K., et al., 2002. Tuning support vector machines for biomedical named entity recognition. Association for Computation Linguistics Workshop on Natural …. [Cited by 111] (20.03/year)
  74. KUDOH, T. and Y. MATSUMOTO, 2000. Use of support vector learning for chunk identification. Proceedings of the 2nd workshop on Learning language in …. [Cited by 122] (16.18/year)
  75. FUNG, G.M. and O.L. MANGASARIAN, 2005. Multicategory Proximal Support Vector Machine Classifiers. Machine Learning. [Cited by 178] (70.06/year)
  76. SUYKENS, J.A.K., et al., 2002. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing. [Cited by 164] (29.60/year)
  77. SCHOLKOPF, B., et al., 1998. Prior knowledge in support vector kernels. Advances in Neural Information Processing Systems. [Cited by 149] (15.62/year)
  78. MANGASARIAN, O.L., 2000. 8 Generalized Support Vector Machines. Advances in Large-Margin Classifiers. [Cited by 139] (18.43/year)
  79. MANGASARIAN, O.L. and D.R. MUSICANT, 2001. Lagrangian Support Vector Machines. Journal of Machine Learning Research. [Cited by 164] (25.07/year)
  80. BURBIDGE, R., et al., 2001. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers and Chemistry. [Cited by 166] (25.38/year)
  81. MUKHERJEE, S., et al., 1999. Support vector machine classification of microarray data. CBCL Paper. [Cited by 107] (12.53/year)
  82. KEERTHI, S.S., et al., 2000. A fast iterative nearest point algorithm for support vector machineclassifier design. Neural Networks, IEEE Transactions on. [Cited by 166] (22.01/year)
  83. KEERTHI, S.S. and C.J. LIN, 2003. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation. [Cited by 180] (39.64/year)
  84. HEISELE, B., P. HO and T. POGGIO, Proc. 8th International Conference on Computer Vision. Face recognition with support vector machines: Global versus component-based approach. [Cited by 146] (?/year)
  85. GUO, G., S.Z. LI and K. CHAN, 2001. Face recognition by support vector machines. Image and Vision Computing. [Cited by 133] (20.33/year)
  86. TSOCHANTARIDIS, I., et al., 2004. Support vector machine learning for interdependent and structured output spaces. ACM International Conference Proceeding Series. [Cited by 158] (44.62/year)
  87. CRISTIANINI, N. and J. SHAWE-TAYLOR, 2000. Support Vector Machines. [Cited by 128] (16.97/year)
  88. VAPNIK, V. and O. CHAPELLE, 2000. Bounds on Error Expectation for Support Vector Machines. Neural Computation. [Cited by 131] (17.37/year)
  89. BARTLETT, P. and J. SHAWE-TAYLOR, 1999. Generalization Performance of Support Vector Machines and Other Pattern Classifiers. Advances in Kernel Methods: Support Vector Learning. [Cited by 126] (14.75/year)
  90. SCHOLKOPF, B. and A.J. SMOLA, 2002. Learning with Kernels: Support Vector Machines, Regularization. Optimization, and Beyond. MIT Press. [Cited by 185] (33.39/year)
  91. SCHOLKOPF, B., C. BURGES and V. VAPNIK, 1995. Extracting Support Data for a Given Task. Knowledge Discovery and Data Mining. [Cited by 298] (23.76/year)
  92. DECOSTE, D. and B. SCHöLKOPF, 2002. Training Invariant Support Vector Machines. Machine Learning. [Cited by 159] (28.70/year)
  93. ALTUN, Y., I. TSOCHANTARIDIS and T. HOFMANN, 2003. Hidden markov support vector machines. Proc. ICML. [Cited by 136] (29.95/year)
  94. WAHBA, G., Y. LIN and H. ZHANG, 2000. Generalized approximate cross validation for support vector machines. Advances in Large Margin Classifiers. [Cited by 65] (8.62/year)
  95. PHILLIPS, P.J., 1999. Support vector machines applied to face recognition - all 4 versions ». MIT Press Cambridge, MA, USA. [Cited by 121] (14.17/year)
  96. LEE, Y.J. and O.L. MANGASARIAN, 2001. SSVM: A Smooth Support Vector Machine for Classification. Computational Optimization and Applications. [Cited by 133] (20.33/year)
  97. KARCHIN, R., K. KARPLUS and D. HAUSSLER, 2002. Classifying G-protein coupled receptors with support vector machines. Bioinformatics. [Cited by 146] (26.35/year)
  98. CHANG, C.C. and C.J. LIN, 2001. Training ?-Support Vector Classifiers: Theory and Algorithms. Neural Computation. [Cited by 133] (20.33/year)
  99. SUYKENS, J.A.K., L. LUKAS and J. VANDEWALLE, 2000. Sparse approximation using least squares support vector machines. Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. …. [Cited by 82] (10.87/year)
  100. LIN, C.F. and S.D. WANG, 2002. Fuzzy support vector machines. IEEE Transactions on Neural Networks. [Cited by 118] (21.30/year)