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A Review of Distributed Data Models for Learning

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Hybrid Artificial Intelligent Systems (HAIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10334))

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Abstract

This paper deals with aspects of data distribution for machine learning tasks, considering the advantages as well as the drawbacks that are frequently associated with data partitioning and its different models. This study, from the point of view of the distributed data, reviews some of the algorithms that have been used to treat each case, although it is not a review of learning or computation algorithms. Finally, this report looks into the issues that new data partitioning-based models such as MapReduce have brought to distributed learning.

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Acknowledgements

This work was partially supported by the Spanish Ministry of Education and Science under Project TIN2014- 57251-P.

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Authors and Affiliations

  1. Department of Information Technologies, University of Huelva, Huelva, Spain

    Miguel Ángel Rodríguez & Antonio Peregrín

  2. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Alberto Fernández & Francisco Herrera

Authors
  1. Miguel Ángel Rodríguez
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  2. Alberto Fernández
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  3. Antonio Peregrín
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  4. Francisco Herrera
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Corresponding author

Correspondence to Miguel Ángel Rodríguez .

Editor information

Editors and Affiliations

  1. University of La Rioja , Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  2. University of La Rioja , Logroño, La Rioja, Spain

    Rubén Urraca

  3. University of A Coruña , Ferrol, La Coruña, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Rodríguez, M.Á., Fernández, A., Peregrín, A., Herrera, F. (2017). A Review of Distributed Data Models for Learning. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-59650-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59649-5

  • Online ISBN: 978-3-319-59650-1

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