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https://www.researchgate.net/publication/2879212_Regression_Estimation_with_Support_Vector_Learning_Machines
Following the trend, the present study tries to explore the use of different soft computing techniques such as random forest regression, support vector machines (SVM) RBF kernel, SVM poly kernel ...
http://www.kernel-machines.org/publications/Smola96
A. J Smola (1996) . Regression Estimation with Support Vector Learning Machines. Master thesis, Technische Universität München.
https://link.springer.com/article/10.1023%2FB%3ASTCO.0000035301.49549.88
Abstract. In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets.Cited by: 9551
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.3628
BibTeX @MISC{Smola96regressionestimation, author = {Alexander Smola and Chris Burges and Harris Drucker and Steve Golowich and Leo Van Hemmen and Klaus-Robert Müller and Bernhard Schölkopf and Vladimir Vapnik}, title = {Regression Estimation with Support Vector Learning Machines}, year = …
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.
http://citeseerx.ist.psu.edu/showciting?cid=183753
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets.
https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
•Support vector regression •Machine learning tools available. Regression Overview CLUSTERING CLASSIFICATION REGRESSION (THIS TALK) K-means •Decision tree •Linear Discriminant Analysis •Neural Networks •Support Vector Machines •Boosting •Linear Regression •Support Vector Regression ... squared errors Least square estimation ...
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.
http://www.kernel-machines.org/publications/Smola96/bibliography_exportForm
Explains Support Vector Regression with variable cost functions and a large selection of possible kernels (including a description how to implement SV Regression and experimental results). ... Home → Publications → Regression Estimation with Support Vector Learning Machines. Navigation. ... Export Bibliographical Entries. Select the format ...
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