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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/4351

    Título
    Restricted set classification: Who is there?
    Autor
    Kuncheva, Ludmila I. .
    Rodríguez Diez, Juan JoséUBU authority Orcid
    Jackson, Aaron S. .
    Publicado en
    Pattern Recognition. 2017. V. 63. p. 158–170
    Editorial
    Elsevier
    Fecha de publicación
    2017-03
    ISSN
    0031-3203
    Abstract
    We consider a problem where a set X of N objects (instances) coming from c classes have to be classified simultaneously. A restriction is imposed on X in that the maximum possible number of objects from each class is known, hence we dubbed the problem who-is-there? We compare three approaches to this problem: (1) independent classification whereby each object is labelled in the class with the largest posterior probability; (2) a greedy approach which enforces the restriction; and (3) a theoretical approach which, in addition, maximises the likelihood of the label assignment, implemented through the Hungarian assignment algorithm. Our experimental study consists of two parts. The first part includes a custom-made chess data set where the pieces on the chess board must be recognised together from an image of the board. In the second part, we simulate the restricted set classification scenario using 96 datasets from a recently collated repository (University of Santiago de Compostela, USC). Our results show that the proposed approach (3) outperforms approaches (1) and (2).
    Palabras clave
    Pattern recognition
    Object classification
    Restricted set classification
    Compound decision problem
    Chess pieces classification
    Materia
    Computer science
    Informática
    URI
    http://hdl.handle.net/10259/4351
    Versión del editor
    http://dx.doi.org/10.1016/j.patcog.2016.08.028
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