Searching for Bartlett New Support Vector Algorithms information? Find all needed info by using official links provided below.
http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
Peter L. Bartlett RSISE,Australian National University, Canberra 0200, Australia We propose a new class of support vector algorithms for regression and classi” cation. In these algorithms, a parameterºlets one effectively con-trol the number of support vectors. While this can be useful in its own
http://alex.smola.org/papers/2000/SchSmoWilBar00.pdf
Peter L. Bartlett RSISE, Australian National University, Canberra 0200, Australia We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ”lets one effectively con-trol the number of support vectors. While this can be useful in its own
https://www.deepdyve.com/lp/mit-press/new-support-vector-algorithms-4I2gUjGvJh
May 01, 2000 · New Support Vector Algorithms 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 case, and the regularization constant C in …
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 …Cited by: 3121
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.4373
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We describe 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 parametrization has the additional benefit of enabling us to eliminate one of the other ...
https://www.sciencedirect.com/science/article/pii/S0893608009002019
Like the previous v-SVM, the proposed new support vector algorithms with parametric insensitive/margin model have the advantage of using the parameter 0 ≤ v ≤ 1 to control the number of support …Cited by: 97
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.2928
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter º 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 ...
https://www.semanticscholar.org/paper/Shrinking-the-Tube%3A-A-New-Support-Vector-Regression-Sch%C3%B6lkopf-Bartlett/ae59045d34cadb03ddfe65e217ba3b40931ae10a
Bernhard Schölkopf, Peter L. Bartlett, +1 author Robert C. Williamson 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.
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 …
http://www.support-vector-machines.org/SVM_nusvm.html
SVM, support vector machines, SVMC, support vector machines classification, SVMR, support vector machines regression, kernel, machine learning, pattern recognition ...
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