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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
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 …
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 number of support vectors. While this can ...Cited by: 3121
https://www.semanticscholar.org/paper/Shrinking-the-Tube%3A-A-New-Support-Vector-Regression-Sch%C3%B6lkopf-Bartlett/ae59045d34cadb03ddfe65e217ba3b40931ae10a
A new algorithm for Support Vector regression is described. For a priori chosen ν, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction ν of the data points lie outside. Moreover, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally.
https://papers.nips.cc/paper/1563-shrinking-the-tube-a-new-support-vector-regression-algorithm.pdf
Shrinking the Tube: A New Support Vector Regression Algorithm 331 2 ZJ-SV REGRESSION AND c-SV REGRESSION To estimate functions (1) from empirical data (2) we proceed as follows (SchOlkopf et aI.,
https://pdfs.semanticscholar.org/2862/e7b8fefb209cdb4c47a1643f2af71cd67b00.pdf
B. Sch olkopf and A.J. Smola, Support Vector Machines and Kernel Algorithms, 2 INTRODUCTION One of the fundamental problems of learning theory is the following: suppose we are given two classes of objects. We are then faced with a new object, and we have to assign it to one of the two classes. This
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 …
https://www.sciencedirect.com/science/article/pii/S0893608009002019
Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 ≤ v ≤ 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and ...Cited by: 97
http://scholar.google.com/citations?user=DZ-fHPgAAAAJ&hl=en
New articles related to this author's research. Email address for updates. ... New support vector algorithms. B Schölkopf, AJ Smola, RC Williamson, PL Bartlett. Neural computation 12 (5), 1207-1245, 2000. 2915: ... Advances in Kernel Methods: Support Vector …
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.
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