Titlebook: Computational Intelligence in Security for Information Systems 2010; Proceedings of the 3 álvaro Herrero,Emilio Corchado,ángel Alonso Confe

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書目名稱Computational Intelligence in Security for Information Systems 2010影響因子(影響力)




書目名稱Computational Intelligence in Security for Information Systems 2010影響因子(影響力)學科排名




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書目名稱Computational Intelligence in Security for Information Systems 2010網(wǎng)絡公開度學科排名




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書目名稱Computational Intelligence in Security for Information Systems 2010被引頻次學科排名




書目名稱Computational Intelligence in Security for Information Systems 2010年度引用




書目名稱Computational Intelligence in Security for Information Systems 2010年度引用學科排名




書目名稱Computational Intelligence in Security for Information Systems 2010讀者反饋




書目名稱Computational Intelligence in Security for Information Systems 2010讀者反饋學科排名

medieval

BSDT ROC and Cognitive Learning Hypothesis advance, it enables to find some brain’s internal parameters. Here a methodology for BSDT analysis of measured ROC curves has been developed and applied to the fitting of empirical data. It has been demonstrated that BSDT leads naturally to a cognitive (motivational) learning (‘learning-to-be-certa

潰爛

Evolving Fuzzy Classifier for Data Mining - an Information Retrieval Approachnetic programming to evolve a fuzzy classifier in the form of a fuzzy search expression to predict product quality. We interpret the data mining task as a fuzzy information retrieval problem and we apply successful information retrieval method for search query optimization to the fuzzy classifier ev

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Cervical-Spine

A Multi-agent Data Mining System for Defect Forecasting in a Decentralized Manufacturing Environmentecentralized character of such a plant fits perfectly with the approach of a control system by means of a multi-agent configuration. The agents devoted to rendering the superficial and internal defects maps, to developing and maintaining the learning context, to evaluating the coils entering the pic

追逐

追逐

B-cell

Instinctive

Lipreading Using ,–Gram Feature Vectorted parameters to be used for visual speech classification. We extract the lip contour using edge detection and connectivity analysis. The boundary is defined using six cubic curves. The visual parameters are used to build .-gram feature vectors. Two sets of classification experiments are performed

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