Titlebook: Computational Intelligence; International Joint Kurosh Madani,António Dourado Correia,Joaquim Fili Conference proceedings 2015 Springer In

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书目名称Computational Intelligence影响因子(影响力)




书目名称Computational Intelligence影响因子(影响力)学科排名




书目名称Computational Intelligence网络公开度




书目名称Computational Intelligence网络公开度学科排名




书目名称Computational Intelligence被引频次




书目名称Computational Intelligence被引频次学科排名




书目名称Computational Intelligence年度引用




书目名称Computational Intelligence年度引用学科排名




书目名称Computational Intelligence读者反馈




书目名称Computational Intelligence读者反馈学科排名

Irremediable

Firefly

inconceivable

https://doi.org/10.1007/978-1-4757-6596-0gical gradient. Both the objective measure and the statistical analysis conclude that the idempotent uninorm obtained using the classical negation, and its residual implication is the best configuration in this framework, although some other configurations can also be considered.

形上升才刺激

Inference for Classes of Processes,ion using a Fuzzy Random Forest ensemble based on cross-validation. Through several experiments the approach shows an excellent performance, not only classification accuracy, but also with respect to the number of features selected.

multiply

multiply

Blanch

A Generalisation of the Hyperresolution Principle to First Order Gödel Logicespectively. For an input theory of Gödel logic, the proposed translation produces a so-called admissible order clausal theory. On the basis of the hyperresolution principle, a calculus operating over admissible order clausal theories, is devised. The calculus is proved to be refutation sound and complete for the countable case.

外星人

On the Pair Uninorm-Implication in the Morphological Gradientgical gradient. Both the objective measure and the statistical analysis conclude that the idempotent uninorm obtained using the classical negation, and its residual implication is the best configuration in this framework, although some other configurations can also be considered.

金丝雀

Selecting Features from Low Quality Datasets by a Fuzzy Ensembleion using a Fuzzy Random Forest ensemble based on cross-validation. Through several experiments the approach shows an excellent performance, not only classification accuracy, but also with respect to the number of features selected.