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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://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://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://papers.nips.cc/paper/1238-support-vector-regression-machines.pdf
Support Vector Regression Machines Harris Drucker· Chris J.C. Burges" Linda Kaufman" Alex Smola·· Vladimir Vapoik + *Bell Labs and Monmouth University Department of Electronic Engineering West Long Branch. NJ 07764 **BellLabs + AT&T Labs Abstract A new regression technique based on Vapnik's concept of support vectors is introduced.
https://pdfs.semanticscholar.org/43ff/a2c1a06a76e58a333f2e7d0bd498b24365ca.pdf
Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing· Vladimir Vapnik AT&T Research 101 Crawfords Corner Holmdel, N J 07733 [email protected] Steven E. Golowich Bell Laboratories 700 Mountain Ave. Murray Hill, NJ 07974 [email protected] Abstract Alex Smola· GMD First Rudower Shausee 5
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.
https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
Support Vector Regression •Find a function, f(x), with at most -deviation ... A. J. Smola and B. Scholkopf, A Tutorial on Support Vector Regression, NeuroCOLT Technical Report TR-98-030. SVR Applications •Stock price prediction. SVR Demo. WEKA and linear regression
https://msol.people.uic.edu/ECE516/papers/Accurate%20On-line%20Support%20Vector%20Regression.pdf
2 Support Vector Regression and the Karush-Kuhn Tucker Conditions A more detailed version of the following presentation of SVR theory can be found in Smola and Sch¨olkopf (1998). Given a training set T ={(xi,yi),i = 1···l}, where xi RN, and yi R,we construct a linear regression function, f(x) = WT(x)+b, (2.1) on a feature space F.
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
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