LIBSVM
library for support vector machines
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.
LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.
The SVM learning code from both libraries is often reused in other open source machine learning toolkits, including GATE, KNIME, Orange and scikit-learn.
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.
Begin with the source’s own compact description: “LIBSVM” is library for support vector machines. The dossier treats that line as a proposition to test through LIBSVM, library and support, not as a finished interpretation.
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Three-step research path
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This entry incorporates text from “LIBSVM” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.