research-article

LIBSVM: A library for support vector machines

Authors:

Chih-Chung Chang,

Chih-Jen LinAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 2, Issue 3

Article No.: 27, Pages 1 - 27

https://doi.org/10.1145/1961189.1961199

Published: 06 May 2011 Publication History

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.

References

[1]

Boser, B. E., Guyon, I., and Vapnik, V. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory. ACM Press, 144--152.

Digital Library

[2]

Chang, C.-C. and Lin, C.-J. 2001. Training &nu;-support vector classifiers: Theory and algorithms. Neural Comput. 13, 9, 2119--2147.

Digital Library

[3]

Chang, C.-C. and Lin, C.-J. 2002. Training &nu;-support vector regression: Theory and algorithms. Neural Comput. 14, 8, 1959--1977.

Digital Library

[4]

Chen, P.-H., Fan, R.-E., and Lin, C.-J. 2006. A study on SMO-type decomposition methods for support vector machines. IEEE Trans. Neural Netw. 17, 893--908.

Digital Library

[5]

Chen, P.-H., Lin, C.-J., and Sch&#246;lkopf, B. 2005. A tutorial on &nu;-support vector machines. Appl. Stochas. Models Bus. Indust. 21, 111--136.

Digital Library

[6]

Cortes, C. and Vapnik, V. 1995. Support-vector network. Mach. Learn. 20, 273--297.

Digital Library

[7]

Crisp, D. J. and Burges, C. J. C. 2000. A geometric interpretation of &nu;-SVM classifiers. In Advances in Neural Information Processing Systems, S. Solla, T. Leen, and K.-R. M&#252;ller Eds., Vol. 12, MIT Press, Cambridge, MA.

[8]

Dorff, K. C., Chambwe, N., Srdanovic, M., and Campagne, F. 2010. BDVal: reproducible large-scale predictive model development and validation in high-throughput datasets. Bioinf. 26, 19, 2472--2473.

Digital Library

[9]

Fan, R.-E., Chen, P.-H., and Lin, C.-J. 2005. Working set selection using second order information for training SVM. J. Mach. Learn. Res. 6, 1889--1918.

Digital Library

[10]

Fine, S. and Scheinberg, K. 2001. Efficient svm training using low-rank kernel representations. J. Mach. Learn. Res. 2, 243--264.

Digital Library

[11]

Glasmachers, T. and Igel, C. 2006. Maximum-Gain working set selection for support vector machines. J. Mach. Learn. Res. 7, 1437--1466.

Digital Library

[12]

Grauman, K. and Darrell, T. 2005. The pyramid match kernel: Discriminative classification with sets of image features. In Proceedings of the IEEE International Conference on Computer Vision.

Digital Library

[13]

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., and Pollmann, S. 2009. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinf. 7, 1, 37--53.

[14]

Hsu, C.-W., Chang, C.-C., and Lin, C.-J. 2003. A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University.

[15]

Hsu, C.-W. and Lin, C.-J. 2002a. A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13, 2, 415--425.

Digital Library

[16]

Hsu, C.-W. and Lin, C.-J. 2002b. A simple decomposition method for support vector machines. Mach. Learn. 46, 291--314.

Digital Library

[17]

Joachims, T. 1998. Making large-scale SVM learning practical. In Advances in Kernel Methods -- Support Vector Learning, B. Sch&#246;lkopf, C. J. C. Burges, and A. J. Smola, Eds., MIT Press, Cambridge, MA, 169--184.

Digital Library

[18]

Keerthi, S. S., Chapelle, O., and DeCoste, D. 2006. Building support vector machines with reduced classifier complexity. J. Mach. Learn. Res. 7, 1493--1515.

Digital Library

[19]

Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., and Murthy, K. R. K. 2001. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput. 13, 637--649.

Digital Library

[20]

Knerr, S., Personnaz, L., and Dreyfus, G. 1990. Single-Layer learning revisited: A stepwise procedure for building and training a neural network. In Neurocomputing: Algorithms, Architectures and Applications, J. Fogelman, Ed. Springer.

[21]

Kressel, U. H.-G. 1998. Pairwise classification and support vector machines. In Advances in Kernel Methods -- Support Vector Learning, B. Sch&#246;lkopf, C. J. C. Burges, and A. J. Smola, Eds., MIT Press, Cambridge, MA, 255--268.

Digital Library

[22]

Lee, Y.-J. and Mangasarian, O. L. 2001. RSVM: Reduced support vector machines. In Proceedings of the 1st SIAM International Conference on Data Mining.

[23]

Lin, C.-J. and Weng, R. C. 2004. Simple probabilistic predictions for support vector regression. Tech. rep., Department of Computer Science, National Taiwan University.

[24]

Lin, H.-T., Lin, C.-J., and Weng, R. C. 2007. A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 68, 267--276.

Digital Library

[25]

List, N. and Simon, H. U. 2007. General polynomial time decomposition algorithms. J. Mach. Learn. Res. 8, 303--321.

Digital Library

[26]

List, N. and Simon, H. U. 2009. SVM-Optimization and steepest-descent line search. In Proceedings of the 22nd Annual Conference on Computational Learning Theory.

[27]

Nivre, J., Hall, J., Nilsson, J., Chanev, A., Eryigit, G., Kubler, S., Marinov, S., and Marsi, E. 2007. MaltParser: A language-independent system for data-driven dependency parsing. Natural Lang. Engin. 13, 2, 95--135.

[28]

Osuna, E., Freund, R., and Girosi, F. 1997a. Support vector machines: Training and applications. AI Memo 1602, Massachusetts Institute of Technology.

Digital Library

[29]

Osuna, E., Freund, R., and Girosi, F. 1997b. Training support vector machines: An application to face detection. In Proceedings of CVPR'97. IEEE, Los Alamitos, CA, 130--136.

Digital Library

[30]

Palagi, L. and Sciandrone, M. 2005. On the convergence of a modified version of SVM^light algorithm. Optimiz. Methods Softw. 20, 2--3, 315--332.

[31]

Platt, J. C. 1998. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods - Support Vector Learning, B. Sch&#246;lkopf, C. J. C. Burges, and A. J. Smola, Eds. MIT Press, Cambridge, MA.

Digital Library

[32]

Platt, J. C. 2000. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Sch&#246;lkopf, and D. Schuurmans, Eds. MIT Press, Cambridge, MA.

[33]

Sch&#246;lkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C. 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 7, 1443--1471.

Digital Library

[34]

Sch&#246;lkopf, B., Smola, A., Williamson, R. C., and Bartlett, P. L. 2000. New support vector algorithms. Neural Comput. 12, 1207--1245.

Digital Library

[35]

Segata, N. and Blanzieri, E. 2010. Fast and scalable local kernel machines. J. Mach. Learn. Res. 11, 1883--1926.

Digital Library

[36]

Vapnik, V. 1998. Statistical Learning Theory. Wiley, New York.

[37]

Wu, T.-F., Lin, C.-J., and Weng, R. C. 2004. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975--1005.

Digital Library

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LIBSVM: A library for support vector machines

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 2, Issue 3

April 2011

259 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/1961189

Issue’s Table of Contents

Copyright © 2011 ACM.

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Association for Computing Machinery

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Publication History

Published: 06 May 2011

Accepted: 01 February 2011

Received: 01 January 2011

Published in TIST Volume 2, Issue 3

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Classification LIBSVM optimization regression support vector machines SVM

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Ferrante MDe Marco PRampado OGianusso LOriggi D(2025)Effective Dose Estimation in Computed Tomography by Machine LearningTomography10.3390/tomography1101000211:1(2)Online publication date: 2-Jan-2025
https://doi.org/10.3390/tomography11010002
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