Support Vector Regression

https://www.mathworks.com/help/stats/understanding...
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://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html
Support Vector Machine for regression implemented using libsvm using a parameter to control the number of support vectors LinearSVR Scalable Linear Support Vector Machine for regression implemented using liblinear

https://www.analyticsvidhya.com/blog/2020/03/...
Implementing Support Vector Regression SVR in Python Step 1 Importing the libraries Step 2 Reading the dataset Step 3 Feature Scaling A real world dataset contains features that vary in magnitudes units and range I would Step 4 Fitting SVR to the dataset Kernel is the most

https://en.wikipedia.org/wiki/Support_vector_machine
In machine learning support vector machines SVMs also support vector networks are supervised max margin models with associated learning algorithms that analyze data for classification and regression analysis

https://link.springer.com/chapter/10.1007/978-1-4302-5990-9_4
As in classification support vector regression SVR is characterized by the use of kernels sparse solution and VC control of the margin and the number of support vectors Although less popular than SVM SVR has been proven to be an effective tool in real value function estimation

https://scikit-learn.org/stable/modules/svm
Support vector machines SVMs are a set of supervised learning methods used for classification regression and outliers detection The advantages of support vector machines are Effective in high dimensional spaces Still effective in cases where number of dimensions is greater than the number of samples

https://www.sciencedirect.com/science/article/pii/B9780128157398000079
Support vector regression SVR is a supervised machine learning technique to handle regression problems Drucker et al 1997Vapnik 1998 Regression analysis is useful to analyze the relationship between a dependent variable and one or more predictor variables

https://link.springer.com/article/10.1023/B:STCO.0000035301.49549.88
1 Mention Explore all metrics Abstract In this tutorial we give an overview of the basic ideas underlying Support Vector SV machines for function estimation

https://link.springer.com/content/pdf/10.1007/978-1...
As in classification support vector regression SVR is characterized by the use of kernels sparse solution and VC control of the margin and the number of support vectors Although less popular than SVM SVR has been proven to be an effective tool in real value function estimation