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Application of the fuzzy min-max neural network classifier to problems with continuous and discrete attributes

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dc.contributor.author Likas, A en
dc.contributor.author Blekas, K en
dc.contributor.author Stafylopatis, A en
dc.date.accessioned 2014-03-01T02:41:00Z
dc.date.available 2014-03-01T02:41:00Z
dc.date.issued 1994 en
dc.identifier.uri http://hdl.handle.net/123456789/30317
dc.subject Fuzzy Set en
dc.subject Neural Network Classifier en
dc.subject Pattern Recognition en
dc.subject.other Artificial intelligence en
dc.subject.other Computational complexity en
dc.subject.other Computational methods en
dc.subject.other Fuzzy sets en
dc.subject.other Learning systems en
dc.subject.other Mathematical models en
dc.subject.other Pattern recognition en
dc.subject.other Discrete dimensions en
dc.subject.other Fuzzy min max classification network en
dc.subject.other Hyperbox fuzzy sets en
dc.subject.other Network training en
dc.subject.other Neural networks en
dc.title Application of the fuzzy min-max neural network classifier to problems with continuous and discrete attributes en
heal.type conferenceItem en
heal.identifier.primary 10.1109/NNSP.1994.366052 en
heal.identifier.secondary http://dx.doi.org/10.1109/NNSP.1994.366052 en
heal.publicationDate 1994 en
heal.abstract The fuzzy min-max classification network constitutes a promising pattern recognition approach that is based on hyberbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The definition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max network to a problem with continuous and discrete attributes, requires the modification of its definition and operation in order to deal with the discrete dimensions. Experimental results using the modified model on a difficult pattern recognition problem establishes the strengths and weaknesses of the proposed approach. en
heal.publisher IEEE, Piscataway, NJ, United States en
heal.journalName Neural Networks for Signal Processing - Proceedings of the IEEE Workshop en
dc.identifier.doi 10.1109/NNSP.1994.366052 en
dc.identifier.spage 163 en
dc.identifier.epage 170 en


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