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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://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.
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
https://dblp.uni-trier.de/rec/journals/sac/SmolaS04
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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.
http://weka.sourceforge.net/doc.stable/weka/classifiers/functions/supportVector/RegSMO.html
public class RegSMO extends RegOptimizer implements TechnicalInformationHandler Implementation of SMO for support vector regression as described in : A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.
https://www.semanticscholar.org/paper/A-tutorial-on-support-vector-regression-Smola-Sch%C3%B6lkopf/06bb5771e6b8a9356c5f4ae28c98b4397c043349
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.
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