Properties of measures of information in evidence and possibility theories
Fuzzy Sets and Systems - Special Issue: Measures of Uncertainty
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
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
Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation
IEEE Transactions on Knowledge and Data Engineering
Expectation Maximization for Weakly Labeled Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Using dempster-shafer theory in MCF systems to reject samples
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Statistical Parameter Identification of Analog Integrated Circuit Reverse Models
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
IPRA: Regressing Data with Independent Parameters
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Building granular fuzzy decision support systems
Knowledge-Based Systems
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We introduce a regression method that fully exploits both global and local information about a set of points in search of a suitable function explaining their mutual relationships. The points are assumed to form a repository of information granules. At a global level, statistical methods discriminate between regular points and outliers. Then the local component of the information embedded in the former is used to draw an optimal regression curve. We address the challenge of using a variety of standard machine learning tools such as support vector machine (SVM) or slight variants of them within the unifying hat of Granular Computing realm to obtain a definitely new featured nonlinear regression method. The performance of the proposed approach is illustrated with the aid of three well-known benchmarks and ad hoc featured datasets.