LIBSVM -- A Library for Support Vector Machines
Chih-Chung Chang and Chih-Jen Lin Version 3.33 released on July 11, 2024. We fix some minor bugs. Version 3.31 released on February 28, 2023. Probabilistic outputs for one-class SVM are now supported. Version 3.25 released on April 14, 2021. Installing the Python interface through PyPI is supported > pip install -U libsvm-official The python directory is re-organized so >>> from libsvm.svmutil imp
SVR
SVR# class sklearn.svm.SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)[source]# Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard
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https://www.csie.ntu.edu.tw/~r94100/libsvm-2.8/README
