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https://www.di.ens.fr/~mallat/papiers/svmtutorial.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://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.
https://link.springer.com/article/10.1023%2FA%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. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global.
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://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=29742
Burges, C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
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.semanticscholar.org/paper/A-Tutorial-on-Support-Vector-Machines-for-Pattern-Burges/fe84db9e87a513b285ab32147cd901782e66616d/figure/2
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.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.1083
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.
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. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global.
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
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