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    Interpretability of artificial hydrocarbon networks for breast cancer classification 

    Ponce, Hiram; Martinez-Villaseñor, Lourdes (Institute of Electrical and Electronics Engineers Inc., 2017)
    In machine learning, interpretability refers to understand the underlying behavior of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to ...

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    Author
    HIRAM EREDIN PONCE ESPINOSA;376768 (1)
    Martinez-Villaseñor, Lourdes (1)MARÍA DE LOURDES GUADALUPE MARTÍNEZ VILLASEÑOR;241561 (1)
    Ponce, Hiram (1)
    Subject
    Artificial intelligence (1)
    Breast cancer classifications (1)
    Decision making (1)
    Decision making process (1)Decision support systems (1)Diagnosis (1)Diagnosis criteria (1)
    Diseases (1)
    Economic and social effects (1)
    Hydrocarbons (1)
    ... View MoreDate Issued
    2017 (1)
    Has File(s)
    false (1)

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