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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://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
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://mitpress.mit.edu/contributors/christopher-jc-burges
Support Vector Learning Christopher J.C. Burges , Bernhard Schölkopf , and Alexander J. Smola 1998 The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion.
https://www.semanticscholar.org/paper/A-Tutorial-on-Support-Vector-Machines-for-Pattern-Burges/fe84db9e87a513b285ab32147cd901782e66616d
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
Jun 01, 1998 · 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.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 ...
http://jmvidal.cse.sc.edu/lib/burges98a.html
Cited by 2342 - Google Scholar @article{ burges98a, author = {Christopher J. C. Burges}, title = {A Tutorial on Support Vector Machines for Pattern Recognition}, journal = {Data Mining and Knowledge Discovery}, volume = 2, number = 2, pages = "121--167", year = 1998, abstract = {The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization.
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