MapReduce SVM Game

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

doi:10.1016/j.procs.2015.07.307

Title: MapReduce SVM Game

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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 anmore »« less

Authors:

Vineyard, Craig M. ^[1]; Verzi, Stephen J. ^[1]; James, Conrad D. ^[2]; Aimone, James B. ^[2]; Heileman, Gregory L. ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of New Mexico, Albuquerque, NM (United States)

Publication Date:: Mon Aug 10 00:00:00 EDT 2015

Research Org.:: Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1214697

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: Procedia Computer Science

Additional Journal Information:: Journal Volume: 53; Journal Issue: C; Journal ID: ISSN 1877-0509

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Support vector machine; Game theory; Machine learning; MapReduce

Citation Formats


                    Vineyard, Craig M., Verzi, Stephen J., James, Conrad D., Aimone, James B., and Heileman, Gregory L. MapReduce SVM Game.  United States: N. p., 2015. 
Web.  doi:10.1016/j.procs.2015.07.307.

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                    Vineyard, Craig M., Verzi, Stephen J., James, Conrad D., Aimone, James B., & Heileman, Gregory L. MapReduce SVM Game.  United States.  https://doi.org/10.1016/j.procs.2015.07.307

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                    Vineyard, Craig M., Verzi, Stephen J., James, Conrad D., Aimone, James B., and Heileman, Gregory L. Mon .  
"MapReduce SVM Game".  United States.  https://doi.org/10.1016/j.procs.2015.07.307.  https://www.osti.gov/servlets/purl/1214697.

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@article{osti_1214697,

  title        = {MapReduce SVM Game},

  author       = {Vineyard, Craig M. and Verzi, Stephen J. and James, Conrad D. and Aimone, James B. and Heileman, Gregory L.},

  abstractNote = {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.},

  doi          = {10.1016/j.procs.2015.07.307},

  journal      = {Procedia Computer Science},

  number       = C,

  volume       = 53,

  place        = {United States},

  year         = {Mon Aug 10 00:00:00 EDT 2015},

  month        = {Mon Aug 10 00:00:00 EDT 2015}

}

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