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    • Revista IJIMAI
    • 2015
    • vol. 3, nº 5, december 2015
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2015
    • vol. 3, nº 5, december 2015
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    Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

    Autor: 
    Cueva-Lovelle, Juan Manuel
    ;
    García-Díaz, Vicente
    ;
    Pelayo G-Bustelo, Cristina
    ;
    Pascual-Espada, Jórdan
    Fecha: 
    2015
    Palabra clave: 
    MDE; DSL; artificial intelligence; machine learning; Xtext; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/10169
    DOI: 
    https://doi.org/10.9781/ijimai.2015.351
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2508
    Open Access
    Resumen:
    Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.
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