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

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書目名稱Computational Intelligence影響因子(影響力)




書目名稱Computational Intelligence影響因子(影響力)學科排名




書目名稱Computational Intelligence網(wǎng)絡公開度




書目名稱Computational Intelligence網(wǎng)絡公開度學科排名




書目名稱Computational Intelligence被引頻次




書目名稱Computational Intelligence被引頻次學科排名




書目名稱Computational Intelligence年度引用




書目名稱Computational Intelligence年度引用學科排名




書目名稱Computational Intelligence讀者反饋




書目名稱Computational Intelligence讀者反饋學科排名

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 G?del Logicespectively. For an input theory of G?del 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.

金絲雀

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.