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Mexican International Conference on Artificial Intelligence

MICAI 2017: Advances in Soft Computing pp 3–16Cite as

Versatility of Artificial Hydrocarbon Networks for Supervised Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10632))

Abstract

Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning task, and it is difficult to comply with all possible desirable features of each particular domain. However, it is important that a new technique comply with the most requirements and desirable features of as many domains and learning tasks as possible. In this paper, we presented artificial hydrocarbon networks (AHN) as versatile and efficient supervised learning method. We determined the ability of AHN to solve different problem domains, with different data-sources and to learn different tasks. The analysis considered six applications in which AHN was successfully applied.

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920, Mexico City, Mexico

    Hiram Ponce & Ma Lourdes Martínez-Villaseñor

Authors
  1. Hiram Ponce
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  2. Ma Lourdes Martínez-Villaseñor
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Corresponding author

Correspondence to Hiram Ponce .

Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Pachuca, Mexico

    Félix Castro

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

  3. Tecnológico de Monterrey, Atizapán de Zaragoza, Mexico

    Miguel González-Mendoza

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Ponce, H., Martínez-Villaseñor, M.L. (2018). Versatility of Artificial Hydrocarbon Networks for Supervised Learning. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-02837-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02836-7

  • Online ISBN: 978-3-030-02837-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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