2001 LIBSVM

• (Chang & Lin, 2001) ⇒ Chih-Chung Chang, and Chih-Jen Lin. (2001). “LIBSVM: a library for support vector machines." Technical Report.

Cited By

• ~7,055 http://scholar.google.com/scholar?q=%22LIBSVM%3A+a+library+for+support+vector+machines%22+2001

2004

• (Hastie et al., 2004) ⇒ Trevor Hastie, Saharon Rosset, Robert Tibshirani, and Ji Zhu. (2004). “The Entire Regularization Path for the Support Vector Machine.” In: The Journal of Machine Learning Research, 5.
  • QUOTE: It seems that the regularization parameter C (or l) is often regarded as a genuine “nuisance” in the community of SVM users. Software packages, such as the widely used SVMlight (Joachims, 1999), provide default settings for C, which are then used without much further exploration. A recent introductory document (Hsu et al., 2003) supporting the LIBSVM package does encourage grid search for C.

References

• B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144{152. ACM Press, 1992.
• C.-C. Chang and C.-J. Lin. Training v-support vector classifiers: Theory and algorithms. Neural Computation, 13(9):2119{2147, 2001.
• P.-H. Chen, R.-E. Fan, and C.-J. Lin. A study on SMO-type decomposition methods for support vector machines. IEEE Transactions on Neural Networks, 17:893{908, July 2006. URL http://www.csie.ntu.edu.tw/~cjlin/papers/generalSMO.pdf.
• C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273{297,1995.
• D. J. Crisp and C. J. C. Burges. A geometric interpretation of v-SVM classifiers. In S. Solla, T. Leen, and K.-R. M?uller, editors, Advances in Neural Information Processing Systems, volume 12, Cambridge, MA, 2000. MIT Press.
• R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research, 6:1889{1918, 2005. URL http://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf.
• J. Friedman. Another approach to polychotomous classification. Technical report, Department of Statistics, Stanford University, 1996. Available at http://www-stat.stanford.edu/reports/friedman/poly.ps.Z
• C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415{425, 2002.
• T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.
• S. Knerr, L. Personnaz, and G. Dreyfus. Single-layer learning revisited: a stepwise procedure for building and training a neural network. In J. Fogelman, editor, Neurocomputing: Algorithms, Architectures and Applications. Springer-Verlag, 1990.
• U. Kressel. Pairwise classification and support vector machines. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods | Support Vector Learning, pages 255{268, Cambridge, MA, 1999. MIT Press.
• C.-J. Lin and R. C. Weng. Simple probabilistic predictions for support vector regression. Technical report, Department of Computer Science, National Taiwan University, 2004. URL http://www.csie.ntu.edu.tw/~cjlin/papers/svrprob.pdf.
• H.-T. Lin, C.-J. Lin, and R. C. Weng. A note on Platt's probabilistic outputs for support vector machines. Technical report, Department of Computer Science, National Taiwan University, 2003. URL http://www.csie.ntu.edu.tw/~cjlin/papers/plattprob.ps.
• E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In: Proceedings of CVPR'97, pages 130{136, New York, NY, 1997a. IEEE.
• E. Osuna, R. Freund, and F. Girosi. Support vector machines: Training and applications. AI Memo 1602, Massachusetts Institute of Technology, 1997b.
• J. Platt. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In Alexander J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, Cambridge, MA, 2000. MIT Press. URL citeseer.nj.nec.com/platt99probabilistic.html.
• J. C. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.
• B. Schölkopf, Alexander J. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12:1207{1245, 2000.
• B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443{1471, 2001.
• V. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.
• T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5:975{1005, 2004. URL http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2001 LIBSVM	Chih-Jen Lin Chih-Chung Chang			LIBSVM: a library for support vector machines			http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf