LIBSVM System
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LIBSVM System is an SVM-centric machine learning tool.
- Context:
- It can implement:
- It requires Binarized Categorical Data.
- Example(s):
- libsvm v3.22 (2016-12-22).
- https://github.com/cjlin1/libsvm/releases
- Counter-Example(s):
- See: Regularization Parameter.
References
2011
- (Chang & Lin, 2011) ⇒ Chih-Chung Chang, and Chih-Jen Lin. (2011). “LIBSVM: A Library for Support Vector Machines.” In: ACM Transactions on Intelligent Systems and Technology (TIST) Journal, 2(3). doi:10.1145/1961189.1961199
2009
- LIBSVM System http://www.csie.ntu.edu.tw/~cjlin/libsvm/
- LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
2005
- (Fan et al., 2005) ⇒ R.-E. Fan, P.-H. Chen, and Chih-Jen Lin. (2005). “Working Set Selection using Second Order Information for Training SVM.” In: Journal of Machine Learning Research 6
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.
2003
- (Hsu et al., 2003) ⇒ Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin. (2003). “A Practical Guide to Support Vector Classification." Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2003.
2001
- (Chang & Lin, 2001) ⇒ Chih-Chung Chang, and Chih-Jen Lin. (2001). “LIBSVM: a library for support vector machines."