Robustness And Regularization Of Support Vector Machines

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Robustness and Regularization of Support Vector Machines

    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.

Robustness and Regularization of Support Vector Machines ...

    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.

Robustness and Regularization of Support Vector Machines

    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

Robustness, Risk, and Regularization in Support Vector ...

    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.

Robustness, Risk, and Regularization in Support Vector ...

    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

Robustness and Regularization of Support Vector Machines

    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).

Robustness and regularization of support vector machines ...

    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

On Robustness and Regularization of Structural Support ...

    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-

Robustness and regularization of support vector machines ...

    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.

Comment on ”Robustness and Regularization of Support ...

    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.

Robustness and regularization of support vector machines ...

    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

Robustness and regularization of support vector machines

    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 ...

"Robustness and Regularization of Support Vector Machines."

    https://dblp.uni-trier.de/rec/journals/jmlr/XuCM09
    Bibliographic details on Robustness and Regularization of Support Vector Machines.

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).

CiteSeerX — Robustness and Regularization of SVMs ...

    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 ...

Robustness and Regularization of Support Vector Machines

    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.

Comment on ”Robustness and Regularization of Support ...

    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.

On Robustness and Regularization of Structural Support ...

    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 …

Comment on "robustness and regularization of support ...

    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.

On Robustness and Regularization of Structural Support ...

    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.

Support Vector Machine and regularization

    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.

Robustness and Regularization of Support Vector Machines ...

    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.

Robustness, Risk & Regularization in SVMs Robustness, Risk ...

    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...

CiteSeerX — Robustness and Regularization of SVMs ...

    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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