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https://www.di.ens.fr/~mallat/papiers/svmtutorial.pdf
CHRISTOPHER J.C. BURGES [email protected] Bell Laboratories, Lucent Technologies Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail.
http://svms.org/tutorials/Burges1998.pdf
A Tutorial on Support Vector Machines for Pattern Recognition CHRISTOPHER J.C. BURGES [email protected] Bell Laboratories, Lucent Technologies Editor: Usama Fayyad Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization.
http://people.csail.mit.edu/dsontag/courses/ml14/notes/burges_SVM_tutorial.pdf
A Tutorial on Support Vector Machines for Pattern Recognition CHRISTOPHER J.C. BURGES [email protected] Bell Laboratories, Lucent Technologies Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable
https://link.springer.com/article/10.1023%2FA%3A1009715923555
Jun 01, 1998 · G. Wahba. Support vector machines, reproducing kernel hilbert spaces and the randomized gacv. In Advances in Kernel Methods-Support Vector Learning, Bernhard Schölkopf, Christopher J.C. Burges and Alexander J. Smola (eds.), MIT …Cited by: 21704
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.1083
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): . The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are ...
https://www.cs.umd.edu/~samir/498/SVM.pdf
A tutorial on support vector machines for pattern recognition. C.J.C. Burges. ... i are support vectors with non-zero Lagrangian multipliers. Both in the dual formulation of the problem and in the solution training points appear only inside dot products Linear SVMs: Overview ...
https://www.microsoft.com/en-us/research/publication/a-tutorial-on-support-vector-machines-for-pattern-recognition/
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global.Cited by: 21704
https://dl.acm.org/citation.cfm?id=593463
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. ... Burges, C. J. C. Simplified support vector decision rules. In Lorenza Saitta, ...Cited by: 21704
https://dl.acm.org/doi/10.1023/A%3A1009715923555
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail.
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
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