Interpolating support information granules
We 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.