Nonlinear support vector machines through iterative majorization and I-splines

Patrick J.F. Groenen; Georgi Nalbantov; J. Cor Bioch

doi:10.1007/978-3-540-70981-7_18

Nonlinear support vector machines through iterative majorization and I-splines

Patrick J.F. Groenen, Georgi Nalbantov, J. Cor Bioch

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

1 Citation (Scopus)

Abstract

To minimize the primal support vector machine (SVM) problem, we propose to use iterative majorization. To allow for nonlinearity of the predictors, we use (non)monotone spline transformations. An advantage over the usual kernel approach in the dual problem is that the variables can be easily interpreted. We illustrate this with an example from the literature.

Original language	English
Title of host publication	Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006
Pages	149-161
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-540-70981-7_18
Publication status	Published - 2007
Event	30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006 - Berlin, Germany Duration: 8 Mar 2006 → 10 Mar 2006

Publication series

Series	Studies in Classification, Data Analysis, and Knowledge Organization
ISSN	1431-8814

Conference

Conference	30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006
Country/Territory	Germany
City	Berlin
Period	8/03/06 → 10/03/06

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10.1007/978-3-540-70981-7_18

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Groenen, P. J. F., Nalbantov, G., & Bioch, J. C. (2007). Nonlinear support vector machines through iterative majorization and I-splines. In Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006 (pp. 149-161). Studies in Classification, Data Analysis, and Knowledge Organization https://doi.org/10.1007/978-3-540-70981-7_18

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title = "Nonlinear support vector machines through iterative majorization and I-splines",

abstract = "To minimize the primal support vector machine (SVM) problem, we propose to use iterative majorization. To allow for nonlinearity of the predictors, we use (non)monotone spline transformations. An advantage over the usual kernel approach in the dual problem is that the variables can be easily interpreted. We illustrate this with an example from the literature.",

author = "Groenen, {Patrick J.F.} and Georgi Nalbantov and Bioch, {J. Cor}",

year = "2007",

doi = "10.1007/978-3-540-70981-7_18",

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pages = "149--161",

booktitle = "Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006",

note = "30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006 ; Conference date: 08-03-2006 Through 10-03-2006",

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Groenen, PJF, Nalbantov, G & Bioch, JC 2007, Nonlinear support vector machines through iterative majorization and I-splines. in Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 149-161, 30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006, Berlin, Germany, 8/03/06. https://doi.org/10.1007/978-3-540-70981-7_18

Nonlinear support vector machines through iterative majorization and I-splines. / Groenen, Patrick J.F.; Nalbantov, Georgi; Bioch, J. Cor.
Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006. 2007. p. 149-161 (Studies in Classification, Data Analysis, and Knowledge Organization).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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T1 - Nonlinear support vector machines through iterative majorization and I-splines

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AU - Nalbantov, Georgi

AU - Bioch, J. Cor

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AB - To minimize the primal support vector machine (SVM) problem, we propose to use iterative majorization. To allow for nonlinearity of the predictors, we use (non)monotone spline transformations. An advantage over the usual kernel approach in the dual problem is that the variables can be easily interpreted. We illustrate this with an example from the literature.

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BT - Advances in Data Analysis - Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2006

T2 - 30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006

Y2 - 8 March 2006 through 10 March 2006

ER -

Nonlinear support vector machines through iterative majorization and I-splines

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