Fechar
Metadados

@Article{EsmiSussSand:2016:TuEqFu,
               author = "Esmi, Estev{\~a}o and Sussner, Peter and Sandri, Sandra 
                         Aparecida",
          affiliation = "{Universidade Estadual de Campinas (UNICAMP)} and {Universidade 
                         Estadual de Campinas (UNICAMP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Tunable equivalence fuzzy associative memories",
              journal = "Fuzzy Sets and Systems",
                 year = "2016",
               volume = "292",
                pages = "242--260",
                month = "June",
             keywords = "Classification, Fuzzy associative memory, Parametrized equivalence 
                         measure, Selection of fundamental memories, Supervised learning, 
                         Tunable equivalence fuzzy associative memory.",
             abstract = "This paper introduces a new class of fuzzy associative memories 
                         (FAMs) called tunable equivalence fuzzy associative memories, for 
                         short tunable E-FAMs or TE-FAMs, that are determined by the 
                         application of parametrized equivalence measures in the hidden 
                         nodes. Tunable E-FAMs belong to the class of \Θ-FAMs that 
                         have recently appeared in the literature. In contrast to previous 
                         \Θ-FAM models, tunable E-FAMs allow for the extraction of a 
                         fundamental memory set from the training data by means of an 
                         algorithm that depends on the evaluation of equivalence measures. 
                         Furthermore, we are able to optimize not only the weights 
                         corresponding to the contributions of the hidden nodes but also 
                         the contributions of the attributes of the data by tuning the 
                         parametrized equivalence measures used in a TE-FAM model. The 
                         computational effort involved in training tunable TE-FAMs is very 
                         low compared to the one of the previous \Θ-FAM training 
                         algorithm.",
                  doi = "10.1016/j.fss.2015.04.004",
                  url = "http://dx.doi.org/10.1016/j.fss.2015.04.004",
                 issn = "0165-0114",
             language = "en",
           targetfile = "esmi_tunable.pdf",
        urlaccessdate = "27 nov. 2020"
}


Fechar