Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Granular computing in data mining
Data mining and computational intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The Fuzzy Systems Handkbook with Cdrom
The Fuzzy Systems Handkbook with Cdrom
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Interpolating support information granules
Neurocomputing
An experimental analysis of the impact of accuracy degradation in SVM classification
International Journal of Computational Intelligence Studies
Interpolating support information granules
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Tackling outliers in granular box regression
Information Sciences: an International Journal
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We augment a linear regression procedure by a thruth-functional method in order to identify a highly informative regression line. The idea is to use statistical methods to identify a confidence region for the line and exploit the structure of the sample data falling in this region for identifying the most fitting line. The fitness function is related to the fuzziness of the sampled points as a natural extension of the statistical criterion ruling the identification of the confidence region within the Algorithmic Inference approach. We tested the method on three well known benchmarks.