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http://jmlr.csail.mit.edu/papers/volume5/hastie04a/hastie04a.pdf
The support vector machine (SVM) is a widely used tool for classification. Many efficient imple- mentations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters.
https://web.stanford.edu/~hastie/Papers/svmpath.pdf
The Entire Regularization Path for the Support Vector Machine Trevor Hastie∗ Saharon Rosset Rob Tibshirani Ji Zhu March 5, 2004 Abstract The Support Vector Machine is a widely used tool for classification. Many efficient implementations exist for fitting a …
https://web.stanford.edu/~hastie/Papers/NIPS04/nips.pdf
The Entire Regularization Path for the Support Vector Machine Trevor Hastie Department of Statistics Stanford University Stanford, CA 94305, USA [email protected] Saharon Rosset IBM Watson Research Center P.O. Box 218 Yorktown Heights, N.Y. 10598 [email protected] Robert Tibshirani Department of Statistics Stanford University Stanford, CA 94305, USACited by: 108
https://www.researchgate.net/publication/221996122_The_Entire_Regularization_Path_for_Support_Vector_Machines
The search for C is guided by an algorithm 2 proposed by [32], which computes the entire regularization path for the two-class SVM classifier (i.e., all possible values of C for which the solution ...
https://web.stanford.edu/~hastie/Papers/svmpath_jmlr.pdf
The Support Vector Machine is a widely used tool for classification. Many efficient imple- mentations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.391
The support vector machine (SVM) is a widely used tool for classification. Many efficient implementations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.4081
CiteSeerX — The Entire Regularization Path for the Support Vector Machine CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The Support Vector Machine is a widely used tool for classification. Many e#cient implementations exist for fitting a two-class SVM model.
https://web.stanford.edu/~hastie/TALKS/svmpathtalk.pdf
• λ (or C) are regularization parameters, which have to be determined using some means like cross-validation. April 2004 Trevor Hastie, Stanford University 21
https://www.researchgate.net/publication/6452076_The_Entire_Regularization_Path_for_the_Support_Vector_Domain_Description
Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property...
http://dept.stat.lsa.umich.edu/~jizhu/pubs/Hastie-NIPS04.pdf
The Entire Regularization Path for the Support Vector Machine Trevor Hastie Department of Statistics Stanford University Stanford, CA 94305, USA [email protected] Saharon Rosset IBM Watson Research Center P.O. Box 218 Yorktown Heights, N.Y. 10598 [email protected] Robert Tibshirani Department of Statistics Stanford University Stanford, CA 94305, USA
http://dept.stat.lsa.umich.edu/~jizhu/pubs/Hastie-NIPS04.pdf
The Entire Regularization Path for the Support Vector Machine Trevor Hastie Department of Statistics Stanford University Stanford, CA 94305, USA [email protected] Saharon Rosset IBM Watson Research Center P.O. Box 218 Yorktown Heights, N.Y. 10598 [email protected] Robert Tibshirani Department of Statistics Stanford University Stanford, CA ...
https://link.springer.com/chapter/10.1007%2F11866565_30
The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector domain description.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.3121
The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector domain description.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.391
The support vector machine (SVM) is a widely used tool for classification. Many efficient implementations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters.
https://www.researchgate.net/publication/220320285_The_Entire_Regularization_Path_for_the_Support_Vector_Machine
The Entire Regularization Path for the Support Vector Machine Article (PDF Available) in Journal of Machine Learning Research 5:1391-1415 · October 2004 with 59 Reads How we measure 'reads'
https://ieeexplore.ieee.org/document/6178801/
Abstract: The v-support vector classification (v-SVC) proposed by Schölkopf has the advantage of using a regularization parameter v for controlling the number of support vectors and margin errors. However, compared to C-SVC, its formulation is more complicated, and to date there are no effective methods for computing its regularization path.In this paper, we propose a new regularization path ...
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.87.3121&rep=rep1&type=pdf
The Entire Regularization Path for the Support Vector Domain Description Karl Sj¨ostrand1,2 and Rasmus Larsen1 1 Informatics and Mathematical Modelling, Technical University of Denmark 2 Department of Radiology, VAMC, University of California-San Francisco, USA [email protected], [email protected] Abstract. The support vector domain description is a one-class classi-
https://arxiv.org/abs/1610.03738
Abstract: We propose an algorithm for exploring the entire regularization path of asymmetric-cost linear support vector machines. Empirical evidence suggests the predictive power of support vector machines depends on the regularization parameters of the training algorithms.
https://www.researchgate.net/publication/221619141_Computing_the_Solution_Path_for_the_Regularized_Support_Vector_Regression
Computing the Solution Path for the Regularized Support Vector Regression. ... As a final remark, note that, in principle, one can compute the entire regularization path [33, 30, 7, 28, 50] (i.e ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.4144
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we argue that the choice of the SVM cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model.
https://www.sciencedirect.com/science/article/pii/S1361841507000710
This paper has presented an algorithm for efficiently calculating the entire regularization path of the support vector domain description. This means that the classification results for any conceivable choice of the regularization parameter become available.
https://ieeexplore.ieee.org/document/7419254/
Abstract: The v-support vector classification has the advantage of using a regularization parameter v to control the number of support vectors and margin errors. Recently, a regularization path algorithm for v-support vector classification (v-SvcPath) suffers exceptions and singularities in some special cases.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2929880/
Regularization paths for the support-vector machine [Hastie et al., 2004]. The graphical lasso [ Friedman et al., 2008 ] for sparse covariance estimation and undirected graphs Efron et al. [2004] developed an efficient algorithm for computing the entire regularization path for the lasso.
https://www.researchgate.net/publication/6452076_The_Entire_Regularization_Path_for_the_Support_Vector_Domain_Description
A method for multiscale support vector clustering is demonstrated, using the recently emerged method for fast calculation of the entire regularization path of the support vector domain description.
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