LIBSVM explained

A Library for Support Vector Machines
LIBSVM
Developer:Chih-Chung Chang and Chih-Jen Lin
Latest Release Version:3.3
Operating System:Cross-platform
Programming Language:Java, C++
Genre:Machine Learning
License:BSD

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 sequential minimal optimization (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.[4] Bindings and ports exist for programming languages such as Java, MATLAB, R, Julia, and Python. It is available in e1071 library in R and scikit-learn in Python.

Both libraries are free software released under the 3-clause BSD license.[5] [6]

External links

Notes and References

  1. Chang . Chih-Chung. Lin . Chih-Jen. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology. 2 . 3 . 2011. 10.1145/1961189.1961199. 961425.
  2. R.-E. Fan. K.-W. Chang. C.-J. Hsieh. X.-R. Wang. C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research. 9 . 1871–1874 . 2008.
  3. 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 . Orange: data mining toolbox in Python . . 14 . 1 . 2013 . 2349–2353 .
  4. Web site: scikit-learn developers . 1.4. Support Vector Machines . scikit-learn.org . 12 May 2022 . en.
  5. Web site: COPYRIGHT . LIBSVM . National Taiwan University.
  6. Web site: COPYRIGHT . LIBLINEAR . National Taiwan University.