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http://jmlr.csail.mit.edu/papers/volume10/xu09b/xu09b.pdf
Keywords: robustness, regularization, generalization, kernel, support vector machine 1. Introduction Support Vector Machines (SVMs for short) originated in Boser et al. (1992) and can be traced back to as early as Vapnik and Lerner (1963) and Vapnik and Chervonenkis (1974). They continue to be one of the most successful algorithms for classification.
https://www.researchgate.net/publication/1741729_Robustness_and_Regularization_of_Support_Vector_Machines
We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis.
https://dl.acm.org/citation.cfm?id=1755834
We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis.Cited by: 287
http://users.ece.utexas.edu/%7Ecmcaram/pubs/RobustSVMJMLR.pdf
standard norm-regularized support vector machine classifler is a solution to a special case of our flrst formulation, thus providing an explicit link between regularization and robustness in pattern classiflcation. Our second formulation is based on a softer version of robust optimization called comprehensive robustness.
http://users.ece.utexas.edu/~cmcaram/pubs/RobustSVMconf.pdf
show that the standard norm-regularized support vector machine classifier is a solution to a special case of our first formulation, thus providing an ex-plicit link between regularization and robustness in pattern classification. Our second formulation is based on a softer version of robust optimization called comprehensive robustness. We show that
http://opt2008.kyb.tuebingen.mpg.de/papers/xu.pdf
Finally, we show that robustness is a fundamental property of classi- fication algorithms, by re-proving consistency of support vector machines using only robustness arguments (instead of VC dimension or stability).
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.528.1466
At the same time, the physical connection of noise and robustness suggests the potential for a broad new family of robust classification algorithms. Finally, we show that robustness is a fundamental property of classi-fication algorithms, by re-proving consistency of support vector machines using only robustness arguments (instead of VC dimension or stability). 1
http://ix.cs.uoregon.edu/~lowd/icml14torkamani.pdf
ization of support vector machines (SVMs) can be derived from a robust formulation. However, robustness for struc-tured prediction models has remained largely unexplored. Structured prediction problems are characterized by an ex-ponentially large space of possible outputs, such as parse trees or graph labelings, making this a much more chal-
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.415.6281
On the analysis front, the equivalence of robustness and regularization provides a robust optimization interpretation for the success of regularized SVMs. We use this new robustness interpretation of SVMs to give a new proof of consistency of (kernelized) SVMs, thus establishing robustness as the reason regularized SVMs generalize well.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.460.9360&rep=rep1&type=pdf
This paper comments on the published work dealing with robustness and regularization of support vector machines (Journal of Machine Learning Research, Vol. 10, pp. 1485-1510, 2009) by H. Xu et al. They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.528.1466
At the same time, the physical connection of noise and robustness suggests the potential for a broad new family of robust classification algorithms. Finally, we show that robustness is a fundamental property of classi-fication algorithms, by re-proving consistency of support vector machines using only robustness arguments (instead of VC dimension or stability). 1
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.721.3934
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We consider regularized support vector machines (SVMs) and show that they are precisely equiva-lent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis. In terms of algorithms, the equivalence suggests more ...
https://dblp.uni-trier.de/rec/journals/jmlr/XuCM09
Bibliographic details on Robustness and Regularization of Support Vector Machines.
http://videolectures.net/opt08_xu_raros/
Dec 20, 2008 · At the same time, the physical connection of noise and robustness suggests the potential for a broad new family of robust classification algorithms. Finally, we show that robustness is a fundamental property of classi- fication algorithms, by re-proving consistency of support vector machines using only robustness arguments (instead of VC dimension or stability).
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.314.756
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis. In terms of algorithms, the equivalence suggests more ...
http://jmlr.csail.mit.edu/papers/v10/xu09b.html
On the analysis front, the equivalence of robustness and regularization provides a robust optimization interpretation for the success of regularized SVMs. We use this new robustness interpretation of SVMs to give a new proof of consistency of (kernelized) SVMs, thus establishing robustness as the reason regularized SVMs generalize well.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.460.9360&rep=rep1&type=pdf
This paper comments on the published work dealing with robustness and regularization of support vector machines (Journal of Machine Learning Research, Vol. 10, pp. 1485-1510, 2009) by H. Xu et al. They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly.
http://proceedings.mlr.press/v32/torkamani14.html
%0 Conference Paper %T On Robustness and Regularization of Structural Support Vector Machines %A Mohamad Ali Torkamani %A Daniel Lowd %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-torkamani14 %I PMLR %J Proceedings of Machine Learning Research %P 577 …
https://arxiv.org/abs/1308.3750
Abstract: This paper comments on the published work dealing with robustness and regularization of support vector machines (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009) [arXiv:0803.3490] by H. Xu, etc.They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.474.5581
Previous analysis of binary support vector machines (SVMs) has demonstrated a deep connection between robustness to perturbations over uncertainty sets and regularization of the weights. In this paper, we explore the problem of learning robust models for structured prediction problems.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/lec4.pdf
problems involving the desired objective (classification loss in our case) and a regularization penalty. The regularization penalty is used to help stabilize the minimization of the ob jective or infuse prior knowledge we might have about desirable solutions. Many machine learning methods can be viewed as regularization methods in this manner.
https://archive.org/details/arxiv-0803.3490
We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis.
https://www.researchgate.net/publication/220488833_Robustness_Risk_Regularization_in_SVMs_Robustness_Risk_and_Regularization_in_Support_Vector_Machines
We show that the standard norm-regularized support vector machine classifier is a solution to a special case of our first formulation, thus providing an ex- plicit link between regularization and...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.6348
CiteSeerX — Robustness and Regularization of SVMs Robustness and Regularization of Support Vector Machines CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation.
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