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Titlebook: Computational Intelligence and Intelligent Systems; 7th International Sy Kangshun Li,Jin Li,Aniello Castiglione Conference proceedings 2016

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書目名稱Computational Intelligence and Intelligent Systems
副標題7th International Sy
編輯Kangshun Li,Jin Li,Aniello Castiglione
視頻videohttp://file.papertrans.cn/233/232424/232424.mp4
概述Includes supplementary material:
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Computational Intelligence and Intelligent Systems; 7th International Sy Kangshun Li,Jin Li,Aniello Castiglione Conference proceedings 2016
描述.This bookconstitutes the refereed proceedings of the 7th International Symposium onIntelligence Computation and Applications, ISICA 2015, held in Guangzhou,China, in November 2015...The 77revised full papers presented were carefully reviewed and selected from 189submissions. The papers feature the most up-to-date research in analysis andtheory of evolutionary computation, neural network architectures and learning;neuro-dynamics and neuro-engineering; fuzzy logic and control; collectiveintelligence and hybrid systems; deep learning; knowledge discovery; learningand reasoning..
出版日期Conference proceedings 2016
關(guān)鍵詞evolutionary computation; neural network; neuro-dynamics; neuro-engineering; fuzzy logic and control; col
版次1
doihttps://doi.org/10.1007/978-981-10-0356-1
isbn_softcover978-981-10-0355-4
isbn_ebook978-981-10-0356-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Science+Business Media Singapore 2016
The information of publication is updating

書目名稱Computational Intelligence and Intelligent Systems影響因子(影響力)




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1865-0929 n and Applications, ISICA 2015, held in Guangzhou,China, in November 2015...The 77revised full papers presented were carefully reviewed and selected from 189submissions. The papers feature the most up-to-date research in analysis andtheory of evolutionary computation, neural network architectures an
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Predicting Acute Hypotensive Episodes Based on Multi GP (Multi GP). The methodology is applied to the dataset obtained from Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC-II). The achieved accuracy of the proposed methodology is 79.07?% in the training set and 77.98?% in the testing set with the five-fold cross-validation.
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