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Fuzzy non-reducible descriptors

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Języki publikacji
EN
Abstrakty
EN
We present a model for supervised pattern recognition problems in which the features of patterns are fuzzy numbers. Non-Reducible Descriptors (NRDs) for such problems are obtained through the use of a threshold value, which is calculated based on the distance between patterns defined in a manner similar to Hamming distance between binary sequences. Boolean formulas are used to represent these Fuzzy NRDs. This model is useful in a wide variety of applications, and we illustrate its usefulness with a medical application.
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autor
  • Department of Computer Science Park College of Engineering and Aviation, Saint Louis University, 3450 Lindell Boulevard, St. Louis, MO 63103, USA
  • Department of Computer Science Park College of Engineering and Aviation, Saint Louis University, 3450 Lindell Boulevard, St. Louis, MO 63103, USA
Bibliografia
  • [1] Kandel A.: Fuzzy Techniques in Pattern Recognition. John Wiley & Sous. 1982.
  • [2] Dutta-Majumder D.: Fuzzy Mathematical Approach in Pattern Recognition Problems. John Wiley & Sons. 1986.
  • [3] Valev V., Radeva P.: A method of solving pattern or image recognition problem by learning boolean formulas. Proc. of 11th Int. Conf. on Pattern Recognition, Hague, Netherlands, Aug. 30-Sept. 3, IEEE Computer Society Press, II, 359-362. 1992.
  • [4] Valev V., Radeva P.: On the determining of non-reducible descriptors for multidimensional pattern recognition problems. Pattern Recognition and Image Analysis, 3(3), 258-265. 1993.
  • [5] Klir G.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall. 1995.
  • [6] Valev V., Radeva P.: Construction of boolean decision rules for ECG recognition by non-reducible descriptors. Proc. of 13th Int. Conf. on Pattern Recognition, Vienna, Austria, August 25-30, IEEE Computer Society Press, B, 111-115. 1996.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0005-0042
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