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Interpolating support information granules
Bruno Apolloni, Simone Bassis, Dario Malchiodi and Witold Perdycz
In: ICANN 2006, 10-14 Sep 2006, Athens, Greece.
AbstractWe develop a hybrid strategy combing thruth-functionality,
kernel, support vectors and regression to construct highly informative
regression curves. The idea is to use statistical methods to form a confi-
dence region for the line and then exploit the structure of the sample data falling in this region for identifying the most fitting curve. The fitness
function is related to the fuzziness of the sampled points and is regarded
as a natural extension of the statistical criterion ruling the identifica-
tion of the confidence region within the Algorithmic Inference approach.
Its optimization on a non-linear curve passes through kernel methods
implemented via a smart variant of support vector machine techniques.
The performance of the approach is demonstrated for three well-known
benchmarks. [Edit]
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