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https://alex.smola.org/papers/2004/SmoSch04.pdf
Statistics and Computing 14: 199–222, 2004 C 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ RSISE, Australian National University, Canberra 0200, Australia
http://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.Cited by: 9551
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=10.1.1.114.4288
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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.
http://lasa.epfl.ch/teaching/lectures/ML_Phd/Notes/nu-SVM-SVR.pdf
Statistics and Computing 14: 199–222, 2004 C 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ RSISE, Australian National University, Canberra 0200, Australia
http://cmlab.csie.ntu.edu.tw/~cyy/learning/papers/SVR_Tutorial.pdf
A Tutorial on Support Vector Regression Alex J. Smolayand Bernhard Scholkopf¤ z September 30, 2003 Abstract In this tutorial we give an overview of the basic ideas under-lying Support Vector (SV) machines for function estimation.
https://scholar.google.com/citations?user=Tb0ZrYwAAAAJ&hl=en
This "Cited by" count includes citations to the following articles in Scholar. ... Alex Smola. Amazon Web Services. Verified email at smola.org ... 2002: A tutorial on support vector regression. AJ Smola, B Schölkopf. Statistics and computing 14 (3), 199-222, 2004. 9418: 2004: Nonlinear component analysis as a kernel eigenvalue problem. B ...
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://dl.acm.org/doi/10.1023/B%3ASTCO.0000035301.49549.88
Smola A.J. and Schölkopf B. 1998b. A tutorial on support vector regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK. Google Scholar; Smola A.J. and Schölkopf B. 2000. Sparse greedy matrix approximation for machine learning.
https://b-ok.org/book/437027/afc5c8
Main Tutorial on support vector regression. Tutorial on support vector regression Smola A.J., Schoelkopf B. 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 ...
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