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https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html
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 to scale to datasets with more than a couple of 10000 samples. ... Support Vector Machine for regression implemented using ...
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://www.rdocumentation.org/packages/e1071/versions/1.7-3/topics/svm
Support Vector Machines. svm is used to train a support vector machine. It can be used to carry out general regression and classification (of nu and epsilon-type), as well as density-estimation. A formula interface is provided.
https://stats.stackexchange.com/questions/259018/meaning-of-epsilon-in-svm-regression
The value of $\epsilon$ defines a margin of tolerance where no penalty is given to errors. Remember the support vectors are the instances across the margin, i.e. the samples being penalized, which slack variables are non-zero. The larger $\epsilon$ is, the larger errors you admit in your solution.
https://www.saedsayad.com/support_vector_machine_reg.htm
The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. First of all, because output is a real number it becomes very difficult to predict the information at hand, which has infinite possibilities. In the case of regression, a margin of tolerance (epsilon) is set in ...
https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff
Jun 29, 2018 · This post is about SUPPORT VECTOR REGRESSION. Those who are in Machine Learning or Data Science are quite familiar with the term SVM or Support Vector Machine. But SVR is a bit different from SVM…
https://www.researchgate.net/post/In_support_vector_machinesSVM_how_we_adjust_the_parameter_C_why_we_use_this_parameter
In support vector machines (SVM) how can we adjust the parameter C? ... When there are some misclassified patterns then how does C fix them and is C equivalent to epsilon? ... Learning with ...
https://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. Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
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