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Acceleration of Dissimilarity-Based Classification Algorithms Using Multi-core Computation

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Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017 (PAAMS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 619))

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Abstract

The objective of this dissertation proposal will focus on finding the computational structures that allow to adapt costly dissimilarity-based classification algorithms to multi-core architectures for CPU based systems, in order to achieve computational efficiency and improving the accelerations of their corresponding sequential implementations. This paper shows preliminary results of the parallel implementation of the leave-one-out test for the Nearest Feature Line and Rectified Nearest Feature Line Segment classifiers.

A.-L. Uribe-Hurtado—Student Doctorado en Ingeniería - Industria y Organizaciones, Universidad Nacional de Colombia - Sede Manizales.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml.

References

  1. Du, H., Chen, Y.Q.: Rectified nearest feature line segment for pattern classification. Pattern Recogn. 40(5), 1486–1497 (2007)

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Acknowledgments

The fist author acknowledges travel funding to attend PAAMS’17 Doctoral Consortium provided by Universidad Nacional de Colombia through “Convocatoria para la Movilidad Internacional de la Universidad Nacional de Colombia 2016–2018”. Modalidad 2: “Cofinanciación de docentes investigadores o creadores de la Universidad Nacional de Colombia para la presentación de resultados de investigación o representaciones artísticas en eventos de carácter internacional, o para la participación en megaproyectos y concursos internacionales, o para estancias de investigación o residencias artísticas en el extranjero”.

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Authors and Affiliations

  1. Universidad Nacional de Colombia - Sede Manizales - Facultad de Administración - Departamento de Informática y Computación - Grupo de Investigación en Ambientes Inteligentes Adaptativos (GAIA), Campus La Nubia Km 7 vía al Magdalena, Manizales, 170003, Colombia

    Ana-Lorena Uribe-Hurtado & Mauricio Orozco-Alzate

Authors
  1. Ana-Lorena Uribe-Hurtado
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  2. Mauricio Orozco-Alzate
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Corresponding author

Correspondence to Ana-Lorena Uribe-Hurtado .

Editor information

Editors and Affiliations

  1. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Fernando De la Prieta

  2. GECAD Instituto Superior de Engenharia do Porto, Porto, Portugal

    Zita Vale

  3. Department of Informática, Universidade de Lisboa Faculdade de Ciências, Lisboa, Portugal

    Luis Antunes

  4. Facultad de Ciencias, Departamento de Informática y Automática, Salamanca, Spain

    Tiago Pinto

  5. School of Computer Science, Dartmouth College, Hanover, New Hampshire, USA

    Andrew T. Campbell

  6. Departamento de Sistemas Informáticos y Computación, Universidad Politecnica de Valencia, Valencia, Spain

    Vicente Julián

  7. University of Aveiro, Institute of Electronics and Telematics Engineering, Aveiro, Portugal

    Antonio J.R. Neves

  8. Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Salamanca, Salamanca, Spain

    María N. Moreno

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Uribe-Hurtado, AL., Orozco-Alzate, M. (2018). Acceleration of Dissimilarity-Based Classification Algorithms Using Multi-core Computation. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-61578-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61577-6

  • Online ISBN: 978-3-319-61578-3

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