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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://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://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2269990
Smola, A.J. and Scholkopf, B. (2004) A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199-222. ... This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest ...
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://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
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.altmetric.com/details/15477796
A tutorial on support vector regression. ... August 2004. Altmetric Badge. About this Attention Score In the top 25% of all research outputs scored by Altmetric. Among the highest-scoring outputs from this source (#15 of 277) High Attention Score compared to outputs of the same age (89th percentile)
http://www.sciepub.com/reference/93732
A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and computing, vol. 14, pp. 199-222, 2004. has been cited by the following article: Article. Robust Identification of Hydrocarbon Debutanizer Unit using Radial Basis Function Neural Networks (RBFNNs)
https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf
Tutorial on Support Vector Machine (SVM) Vikramaditya Jakkula, School of EECS, Washington State University, Pullman 99164. Abstract: In this tutorial we present a brief introduction to SVM, and we discuss about SVM from published papers, workshop materials & material collected from books and material available online on
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.
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