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http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
New Support Vector Algorithms 1209 Figure 1: In SV regression, a desired accuracyeis speci” ed a priori. It is then attempted to ” t a tube with radiuseto the data. The trade-off between model complexity andpoints lying outside the tube (withpositive slackvariablesj)is determined by minimizing the expression 1.5. subjectto ((w¢xi)Cb)¡yi ...
https://www.mitpressjournals.org/doi/10.1162/089976600300015565
Mar 13, 2006 · We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the …Cited by: 3121
https://dl.acm.org/citation.cfm?id=1139691
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter lets one effectively control the number of support vectors. While this can be...Cited by: 3121
http://alex.smola.org/papers/2000/SchSmoWilBar00.pdf
New Support Vector Algorithms 1209 Figure 1: In SV regression, a desired accuracy "is specified a priori. It is then attempted to fit a tube with radius "to the data. The trade-off between model complexity and points lying outside the tube (with positive slack variables »)is determined by …
https://www.deepdyve.com/lp/mit-press/new-support-vector-algorithms-4I2gUjGvJh
May 01, 2000 · We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter ϵ in the regression ...
https://www.mitpressjournals.org/doi/10.1162/089976602760128081
We discuss the relation betweenɛ-support vector regression (ɛ-SVR) and v-support vector regression (v-SVR).In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) andv-support vector classification (v-SVC).We then discuss some issues that do not occur in the case of classification: the possible range of ɛ and the scaling of target values.Cited by: 313
https://dl.acm.org/doi/10.1162/089976602760128081
Home Browse by Title Periodicals Neural Computation Vol. 14, No. 8 Training v-support vector regression: theory and algorithms article Training v -support vector regression: theory and algorithms
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
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
https://www.semanticscholar.org/paper/New-Support-Vector-Algorithms-Sch%C3%B6lkopf-Smola/8d73c0d0c92446102fdb6cc728b5d69674a1a387
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter in the regression case ...
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