MapReduce SVM game

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

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently and recom-bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.

Original languageEnglish (US)
Pages (from-to)298-307
Number of pages10
JournalProcedia Computer Science
Volume53
Issue number1
DOIs
StatePublished - 2015
Externally publishedYes
EventINNS Conference on Big Data 2015 - San Francisco, United States
Duration: Aug 8 2015Aug 10 2015

Keywords

  • Game theory
  • Machine learning
  • MapReduce
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science(all)

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