Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Evaluation of fuzzy linear regression models
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
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
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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 confidence 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 identification 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.