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Supervised pattern recognition with heterogeneous features

100%

Valev V.

tom Vol. 13, No. 4

345-353

In this paper, we address the supervised pattern recognition problem with heterogeneous features, where the mathematical model is based on construction of thresholds. Non-Reducible Descriptors (NRDs) for fuzzy features are obtained through the use of a threshold value, which is calculated based on the distance between patterns. In case of solving the problem with real features, the mathematical model for construction of thresholds is based on parallel feature partitioning. Boolean formulas are used to represent NRDs.

Fuzzy non-reducible descriptors

63%

Valev V. , Asaithambi A.

tom Vol. 12, No. 3

353-361

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.