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Optimizing Support Vector Regression with Swarm Intelligence for Estimating the Concrete Compression Strength

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10934))

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Abstract

Estimating the compression strength of concrete is a complex problem which has been studied by various researchers. Support Vector Regression (SVR) is a technique that has been shown to be adequate for estimation through input examples. In this paper, we assess three swarm algorithms, Fish School Search (FSS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) aiming to optimize the SVR parameter. The results show that both all swarm-based algorithms far outperformed the original SVR in the concrete compression strength estimation task and the FSS and ABC obtained better results than PSO for this particular problem.

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Authors and Affiliations

  1. University of Pernambuco (UPE), Recife, Pernambuco, Brazil

    Manoel Alves de Almeida Neto, Roberta de Andrade de A. Fagundes & Carmelo J. A. Bastos-Filho

Authors
  1. Manoel Alves de Almeida Neto
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  2. Roberta de Andrade de A. Fagundes
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  3. Carmelo J. A. Bastos-Filho
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Corresponding authors

Correspondence to Manoel Alves de Almeida Neto , Roberta de Andrade de A. Fagundes or Carmelo J. A. Bastos-Filho .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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de Almeida Neto, M.A., de A. Fagundes, R.d.A., Bastos-Filho, C.J.A. (2018). Optimizing Support Vector Regression with Swarm Intelligence for Estimating the Concrete Compression Strength. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-96136-1_11

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