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LIBSVM: A library for support vector machines
Abstract
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,923
Self-citationEstimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056
Self-citationProbabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,834
Making Large-Scale SVM Learning PracticalPreprint
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799
Research works printed but not linked: 9
- 10.1002/asmb.v21:2 · DOI
- 10.1023/A:1022627411411 · DOI
- Joachims T., Advances in Kernel Methods -- Support Vector Learning, B. Schölkopf
- Kressel U. H.-G., Advances in Kernel Methods -- Support Vector Learning, B. Schölkopf
- Lee Y.-J., Proceedings of the 1st SIAM International Conference on Data Mining.
- List N., Proceedings of the 22nd Annual Conference on Computational Learning Theory.
- Osuna E., Proceedings of CVPR'97
- Platt J. C., Advances in Kernel Methods - Support Vector Learning, B. Schölkopf
- Platt J. C., Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D
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Peherstorfer, B., Willcox, K. E., & Gunzburger, M. (2016) · SIAM Journal on Scientific Computing · doi:10.1137/15m1046472 · Cited by 269
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Cinaz, B., Arnrich, B., La Marca, R., & Tröster, G. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0466-1 · Cited by 235
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Dong, B., Li, Z., Rahman, S. M. M., & Vega, R. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2015.09.033 · Cited by 229
Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
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Shin, J. D., Tang, W., & Jadhav, S. P. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.012 · Cited by 199
Kim, S., Shukla, A., & Billard, A. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2014.2316022 · Cited by 220
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Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222
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Bitouk, D., Verma, R., & Nenkova, A. (2010) · Speech Communication · doi:10.1016/j.specom.2010.02.010 · Cited by 219
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Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2169804 · Cited by 221
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Nieder, A., Wagener, L., & Rinnert, P. (2020) · Science · doi:10.1126/science.abb1447 · Cited by 218
Boukerche, A., Tao, Y., & Sun, P. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107484 · Cited by 215
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Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., & Shen, D. (2019) · Scientific Reports · doi:10.1038/s41598-018-37387-9 · Cited by 213
King, J. R., Faugeras, F., Gramfort, A., Schurger, A., El Karoui, I., Sitt, J. D., Rohaut, B., Wacongne, C., Labyt, E., Bekinschtein, T., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.013 · Cited by 197
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Zhao, Q., Qin, X., Zhao, H., & Feng, W. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.04.007 · Cited by 199
Derosa, L., Iebba, V., Silva, C. A. C., Piccinno, G., Wu, G., Lordello, L., Routy, B., Zhao, N., Thelemaque, C., Birebent, R., et al. (2024) · Cell · doi:10.1016/j.cell.2024.05.029 · Cited by 198
López-de-Ipiña, K., Alonso, J.-B., Travieso, C., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M., Ezeiza, A., Barroso, N., Ecay-Torres, M., Martinez-Lage, P., et al. (2013) · Sensors · doi:10.3390/s130506730 · Cited by 190
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Nilashi, M., Ibrahim, O. B., Ahmadi, H., & Shahmoradi, L. (2017) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2017.06.011 · Cited by 190
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Schroder, M., Bevacqua, E., Cowie, R., Eyben, F., Gunes, H., Heylen, D., ter Maat, M., McKeown, G., Pammi, S., Pantic, M., et al. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.34 · Cited by 185
Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2017.2671247 · Cited by 185
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Brodersen, K. H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W. D., Buhmann, J. M., & Stephan, K. E. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.11.002 · Cited by 173
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Tran, T., Nguyen, T. D., Phung, D., & Venkatesh, S. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.01.012 · Cited by 172
Anticevic, A., Hu, X., Xiao, Y., Hu, J., Li, F., Bi, F., Cole, M. W., Savic, A., Yang, G. J., Repovs, G., et al. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2310-14.2015 · Cited by 178
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Le, H., Kabbur, S., Pollastrini, L., Sun, Z., Mills, K., Johnson, K., Karypis, G., & Hu, W.-S. (2012) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2012.08.021 · Cited by 177
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van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm · doi:10.1039/c0md00165a · Cited by 176
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Vlachos, A. (2008) · Computer Speech & Language · doi:10.1016/j.csl.2007.12.001 · Cited by 153
Allefeld, C., Görgen, K., & Haynes, J.-D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.040 · Cited by 157
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Jiang, H., Chen, S., Li, C., Lu, N., Yue, Y., Yin, Y., Zhang, Y., Zhi, X., Zhang, D., & Yuan, Y. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.43 · Cited by 148
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Rehme, A. K., Volz, L. J., Feis, D.-L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S. B., Fink, G. R., & Grefkes, C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu100 · Cited by 117
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Jiang, L., Xuan, J., & Shi, T. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.05.017 · Cited by 103
Zhang, Y., Moorthy, A. K., Chandler, D. M., & Bovik, A. C. (2014) · Signal Processing: Image Communication · doi:10.1016/j.image.2014.05.004 · Cited by 102
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Micera, S., Rossini, P. M., Rigosa, J., Citi, L., Carpaneto, J., Raspopovic, S., Tombini, M., Cipriani, C., Assenza, G., Carrozza, M. C., et al. (2011) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-8-53 · Cited by 100
Cheng, W., Ji, X., Zhang, J., & Feng, J. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00058 · Cited by 97
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Bron, E. E., Steketee, R. M. E., Houston, G. C., Oliver, R. A., Achterberg, H. C., Loog, M., van Swieten, J. C., Hammers, A., Niessen, W. J., Smits, M., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22522 · Cited by 93
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Initiative, T. A. D. N., Zu, C., Jie, B., Liu, M., Chen, S., Shen, D., & Zhang, D. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9480-7 · Cited by 95
Wu, D., Lawhern, V. J., Hairston, W. D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2544108 · Cited by 74
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Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00057 · Cited by 87
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Liu, Y., Yan, W., Han, S., Zhu, H., Tu, Y., Guan, L., & Tan, X. (2022) · Solar RRL · doi:10.1002/solr.202101100 · Cited by 87
MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
ten Kate, M., Redolfi, A., Peira, E., Bos, I., Vos, S. J., Vandenberghe, R., Gabel, S., Schaeverbeke, J., Scheltens, P., Blin, O., et al. (2018) · Alzheimer's Research & Therapy · doi:10.1186/s13195-018-0428-1 · Cited by 78
Meyer, S., Mueller, K., Stuke, K., Bisenius, S., Diehl-Schmid, J., Jessen, F., Kassubek, J., Kornhuber, J., Ludolph, A. C., Prudlo, J., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.02.001 · Cited by 83
Metadata
- Authors
- Chang, C.-C., & Lin, C.-J.
- Affiliations
- National Taiwan University (TW)
- ORCID
- Lin, Chih-Jen
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2011-04
- Volume
- 2
- Issue
- 3
- Pages
- 1-27
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2153635508
- Cited by
- 40,918
- References
- 55
- Citations by year
- 2026: 462 · 2025: 825 · 2024: 916 · 2023: 1155 · 2022: 1487 · 2021: 1910 · 2020: 2233 · 2019: 2526 · 2018: 2850 · 2017: 3142 · 2016: 3468 · 2015: 3562 · 2014: 3302 · 2013: 2936 · 2012: 2306
- FWCI
- 3060.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Funders
- National Science Council Taiwan (NSC 89-2213-E-002-013NSC 89-2213-E-002-106)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- LIBSVM, support vector machine, convergence analysis, multi-class classification, probability estimates, parameter selection, software implementation
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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.
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