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https://alex.smola.org/papers/2004/SmoSch04.pdf
A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ RSISE, Australian National University, Canberra 0200, Australia [email protected] Max-Planck-Institut f¨ur biologische Kybernetik, 72076 T¨ubingen, Germany [email protected] Received July 2002 and accepted November 2003
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://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://b-ok.org/book/437027/afc5c8
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 (or convex) programming part and advanced methods for dealing with large datasets.
http://support-vector-machines.org/SVM_review.html
SVM, support vector machines, SVMC, support vector machines classification, SVMR, support vector machines regression, kernel, machine learning, pattern recognition ...
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.
http://lasa.epfl.ch/teaching/lectures/ML_Phd/Notes/nu-SVM-SVR.pdf
A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ RSISE, Australian National University, Canberra 0200, Australia [email protected] Max-Planck-Institut f¨ur biologische Kybernetik, 72076 T¨ubingen, Germany [email protected] Received July 2002 and accepted November 2003
https://link.springer.com/article/10.1023%2FA%3A1012474916001
The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver.Cited by: 345
http://citeseer.ist.psu.edu/showciting?cid=6782872
A tutorial on support vector regression,” Royal Holloway (1998) by A J Smola, B Schölkopf Add To MetaCart. Tools. Sorted by: Results 1 - 8 of 8. Improvements to the SMO algorithm for SVM regression ... Abstract—In this paper we give a new fast iterative algorithm for …
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.
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