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Kernel regression is more attractive when it is not possible to determine explicit parametric form of the model and moreover, it does not depend on probabilistic distribution. This paper introduces kernel regression in which the input data set is described by interval-value variables. Two model families are considered. The first family estimates the bounds of the intervals regarding either a smooth...
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