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Fuzzy Hyperline Segment Neural Network Pattern Classifier with Different Distance Metrics
2014
International Journal of Computer Applications
The Fuzzy Hyperline Segment Neural Network (FHLSNN) pattern classifier utilizes fuzzy set as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments. The Euclidean distance metric is used to compute the distances to decide the degree of membership function. In this paper, the use of other various distance metrics such as Manhattan, Squared Euclidean, Canberra and Chebyshew distance metrics is proposed. The performance of FHLSNN pattern classifier is evaluated with
doi:10.5120/16612-6450
fatcat:gxulmztfrregnb7bc3h5u7vdmq