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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.9934
CiteSeerX — Simplified Support Vector Decision Rules CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights.
https://www.researchgate.net/publication/2716782_Simplified_Support_Vector_Decision_Rules
Simplified Support Vector Decision Rules. ... All content in this area was uploaded by Christopher J. C. Burges on Jun 10, 2013 ... to be a basic and simple nontrivial basic leadership process [3 ...
https://core.ac.uk/display/24354528
Simplified Support Vector Decision Rules . By Chris J.C. Burges. Abstract. A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights. An SVM is also characterized by a kernel function. Choice of the kernel determines whether the ...Author: Chris J.C. Burges
http://www.kernel-machines.org/publications/Burges96/bibliography_exportForm
Simplified support vector decision rules. Books. Software. Annual Workshop. JMLR. Links. Tutorials. News Call for NIPS 2008 Kernel Learning Workshop Submissions 2008-09-30 Tutorials uploaded 2008-05-13 Machine Learning Summer School / Course On The Analysis On Patterns 2007-02-12 New Kernel-Machines.org server ...
https://www.semanticscholar.org/author/Christopher-J.-C.-Burges/2676309
Semantic Scholar profile for Christopher J. C. Burges, with 2,305 highly influential citations. Semantic Scholar profile for Christopher J. C. Burges, with 2,305 highly influential citations. Skip to search form Skip to main content. ... Simplified Support Vector Decision Rules. Christopher J. C. Burges;
https://link.springer.com/chapter/10.1007/978-3-642-72282-0_12
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces ... Simplified support vector decision rules. In L. Saitta, ... Schölkopf B., Knirsch P., Smola A., Burges C. (1998) Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as ...Cited by: 112
https://link.springer.com/article/10.1023%2FA%3A1009715923555
Burges, C.J.C. Simplified support vector decision rules. In Lorenza Saitta, editor, Proceedings of the Thirteenth International Conference on Machine Learning, pages 71–77, Bari, Italy, 1996. Morgan Kaufman. Google ScholarCited by: 21704
https://link.springer.com/content/pdf/10.1023%2FB%3ASTCO.0000035301.49549.88.pdf
Aug 01, 2004 · 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
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/chapter/10.1007/0-387-37452-3_7
Support Vector Machines is the most recent algorithm in the Machine Learning community. After a bit less than a decade of live, it has displayed many advantages with respect to the best old methods: generalization capacity, ease of use, solution uniqueness.Cited by: 4
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