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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/3862

    Título
    Unsupervised neural models for country and political risk analysis
    Autor
    Herrero Cosío, ÁlvaroAutoridad UBU Orcid
    Corchado, EmilioAutoridad UBU Orcid
    Jiménez Palmero, AlfredoAutoridad UBU
    Publicado en
    Expert Systems with Applications. V. 38, n. 11, p. 13641–1366
    Editorial
    Elsevier
    Fecha de publicación
    2011-10
    ISSN
    0957-4174
    DOI
    10.1016/j.eswa.2011.04.136
    Zusammenfassung
    This interdisciplinary research project focuses on relevant applications of Knowledge Discovery and Artificial Neural Networks in order to identify and analyze levels of country, business and political risk. Its main goal is to help business decision-makers understand the dynamics within the emerging market countries in which they operate. Most of the neural models applied in this study are defined within the framework of unsupervised learning. They are based on Exploratory Projection Pursuit, Topology Preserving Maps and Curvilinear Component Analysis. Two interesting real data sets are analyzed to empirically probe the robustness of these models. The first case study describes information from a significant sample of Spanish multinational enterprises (MNEs). It analyses data pertaining to such aspects as decisions over the location of subsidiary enterprises in various regions across the world, the importance accorded to such decisions and the driving forces behind them. Through a projection-based analysis, this study reveals a range of different reasons underlying the internationalization strategies of Spanish MNEs and the different goals they pursue. It may be concluded that projection connectionist techniques are of immense assistance in the process of identifying the internationalization strategies of Spanish MNEs, their underlying motives and the goals they pursue. The second case study covers several risk categories that include task policy, security, and political stability among others, and it tracks the scores of different countries all over the world. Interesting conclusions are drawn from the application of several business intelligence solutions based on neural projection models, which support data analysis in the context of country and political risk analysis
    Palabras clave
    Neural visualization models
    Exploratory Projection Pursuit
    Unsupervised learning
    Country and political risk
    Business intelligence
    Knowledge extraction
    Materia
    Computer science
    Informática
    URI
    http://hdl.handle.net/10259/3862
    Versión del editor
    http://dx.doi.org/10.1016/j.eswa.2011.04.136
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    Herrero-ESA_2011.pdf
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