Repeated play of the SVM game as a means of adaptive classification

Craig M. Vineyard, Stephen J. Verzi, Conrad D. James, James B. Aimone, Gregory L. Heileman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. An added challenge arises when the problem domain is dynamic or non-stationary with the data distributions or categorizations changing over time. This phenomenon is known as concept drift. Game-theoretic algorithms are often iterative by nature, consisting of repeated game play rather than a single interaction. Effectively, rather than requiring extensive retraining to update a learning model, a game-theoretic approach can adjust strategies as a novel approach to concept drift. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in an adaptive manner with repeated play to address concept drift, and show results of applying this algorithm to synthetic as well as real data.

Original languageEnglish (US)
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
StatePublished - Sep 28 2015
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: Jul 12 2015Jul 17 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks


ConferenceInternational Joint Conference on Neural Networks, IJCNN 2015


  • Games

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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