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Sample-based regularization for support vector machine classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Number of pages6
ISBN (Electronic)9781538618417
Publication statusPublished - 8 Mar 2018
Publication typeA4 Article in a conference publication
EventInternational Conference on Image Processing Theory, Tools and Applications - Montreal, Canada
Duration: 28 Nov 20171 Dec 2017


ConferenceInternational Conference on Image Processing Theory, Tools and Applications


In this paper, we propose a new regularization scheme for the well-known Support Vector Machine (SVM) classifier that operates on the training sample level. The proposed approach is motivated by the fact that Maximum Margin-based classification defines decision functions as a linear combination of the selected training data and, thus, the variations on training sample selection directly affect generalization performance. We show that the exploitation of the proposed regularization scheme is well motivated and intuitive. Experimental results show that the proposed regularization scheme outperforms standard SVM in human action recognition tasks as well as classical recognition problems.


  • Dropout, kernel methods, Regularization, Support Vector Machine

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Field of science, Statistics Finland