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https://www.sciencedirect.com/science/article/pii/S0925231203005307
In this paper, a heuristic rule is utilized to reduce training data for support vector regression (SVR). At first, all the training data are divided into several groups, and then for each group, some training vectors will be discarded based on the measurement of similarity among samples.Cited by: 100
https://www.researchgate.net/publication/220549658_A_heuristic_training_for_support_vector_regression
A heuristic method for accelerating support vector machine (SVM) training based on a measurement of similarity among samples is presented in this paper.
https://www.sciencedirect.com/science/article/abs/pii/S0925231203005307
In this paper, a heuristic rule is utilized to reduce training data for support vector regression (SVR). At first, all the training data are divided into several groups, and then for each group, some training vectors will be discarded based on the measurement of similarity among samples.Cited by: 100
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.
https://en.wikipedia.org/wiki/Support_vector_regression
This method is called support-vector regression (SVR). The model produced by support-vector classification (as described above) depends only on a subset of the training data, because the cost function for building the model does not care about training points that lie beyond the margin.
https://link.springer.com/10.1007/s00521-015-2015-8
Aug 21, 2015 · In order to select the 3PL, integration of the support vector regression (SVR) and self-adaptive ICA (SAICA) has offered a novel model, in which SAICA is utilized to adjust the parameters of the SVR. The suggested model is applied for cosmetics production. Moreover, the comparison of the suggested model and back-propagation neural networks ...Cited by: 5
https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
Linear Regression and Support Vector Regression Paul Paisitkriangkrai [email protected] The University of Adelaide 24 October 2012. Outlines •Regression overview •Linear regression •Support vector regression •Machine learning tools available. ... Given training data
http://ce.sharif.ir/courses/85-86/2/ce725/resources/root/LECTURES/SVM.pdf
ically used to describe classification with support vector methods and support vector regression is used to describe regression with support vector methods. In this report the term SVM will refer to both classification and regression methods, and the terms Support Vector Classification (SVC) and Support Vector Regression (SVR) will be used
https://alex.smola.org/papers/2003/SmoSch03b.pdf
A Tutorial on Support Vector Regression∗ Alex J. Smola†and Bernhard Sch¨olkopf‡ September 30, 2003 Abstract In this tutorial we give an overview of the basic ideas under-lying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algo-rithms for training SV machines, covering both the ...
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.
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