PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Interpolating support information granules
Bruno Apolloni, Simone Bassis, Dario Malchiodi and Witold Perdycz
In: ICANN 2006, 10-14 Sep 2006, Athens, Greece.

Abstract

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.

EPrint Type:Conference or Workshop Item (Paper)
Additional Information:Lecture Notes in Computer Science, Vol 4132
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2283
Deposited By:Dario Malchiodi
Deposited On:22 October 2006