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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://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=100
Alex J. Smola, Bernhard Scholkopf. A Tutorial on Support Vector Regression. NeuroCOLT2 Technical Report Series, 1998.
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://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://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
https://www.semanticscholar.org/paper/A-tutorial-on-support-vector-regression-Smola-Sch%C3%B6lkopf/06bb5771e6b8a9356c5f4ae28c98b4397c043349/figure/6
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://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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