LIBSVM
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| Developer(s) | Chih-Chung Chang and Chih-Jen Lin |
|---|---|
| Stable release | 3.23
/ June 15, 2018 |
| Repository |  |
| Written in | Java, C++ |
| Operating system | Cross-platform |
| Type | Machine Learning |
| License | BSD |
| Website | www |
LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the SMO algorithm for kernelized support vector machines (SVMs), supporting classification and regression.[1] LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.[2]
The SVM learning code from both libraries is often reused in other open source machine learning toolkits, including GATE, KNIME, Orange[3] and scikit-learn. Many bindings to it exist for programming languages such as Java, MATLAB, R, and Python.
Both libraries are free software released under the 3-clause BSD license.[4][5]
References[edit]
- ^ Chang, Chih-Chung; Lin, Chih-Jen (2011). "LIBSVM: A library for support vector machines". ACM Transactions on Intelligent Systems and Technology. 2 (3).
- ^ R.-E. Fan; K.-W. Chang; C.-J. Hsieh; X.-R. Wang; C.-J. Lin (2008). "LIBLINEAR: A Library for Large Linear Classification". Journal of Machine Learning Research. 9: 1871–1874.
- ^ Janez Demšar; Tomaž Curk; Aleš Erjavec; Črt Gorup; Tomaž Hočevar; Mitar Milutinovič; Martin Možina; Matija Polajnar; Marko Toplak; Anže Starič; Miha Stajdohar; Lan Umek; Lan Žagar; Jure Žbontar; Marinka Žitnik; Blaž Zupan (2013). "Orange: data mining toolbox in Python" (PDF). Journal of Machine Learning Research. 14 (1): 2349–2353.
- ^ "COPYRIGHT". LIBSVM. National Taiwan University.
- ^ "COPYRIGHT". LIBLINEAR. National Taiwan University.
External links[edit]
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