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Active-Set Methods for Support Vector Machines

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Support Vector Machines: Theory and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 177))

Abstract

This chapter describes an active-set algorithm for quadratic programming problems that arise from the computation of support vector machines (SVMs). Currently, most SVM optimizers implement working-set (decomposition)techniques because of their ability to handle large data sets. Although these show good results in general, active-set methods are a reasonable alternative - in particular if the data set is not too large, if the problem is ill-conditioned, or if high precision is needed. Algorithms are derived for classification and regression with both fixed and variable bias term. The material is completed by acceleration and approximation techniques as well as a comparison with other optimization methods in application examples.

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Lipo Wang

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Vogt, M., Kecman, V. Active-Set Methods for Support Vector Machines. In: Wang, L. (eds) Support Vector Machines: Theory and Applications. Studies in Fuzziness and Soft Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10984697_6

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  • DOI: https://doi.org/10.1007/10984697_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24388-5

  • Online ISBN: 978-3-540-32384-6

  • eBook Packages: EngineeringEngineering (R0)

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