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Titlebook: Intelligent Computing Systems; First International Anabel Martin-Gonzalez,Victor Uc-Cetina Conference proceedings 2016 Springer Internatio

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書目名稱Intelligent Computing Systems
副標題First International
編輯Anabel Martin-Gonzalez,Victor Uc-Cetina
視頻videohttp://file.papertrans.cn/470/469467/469467.mp4
概述Includes supplementary material:
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Intelligent Computing Systems; First International  Anabel Martin-Gonzalez,Victor Uc-Cetina Conference proceedings 2016 Springer Internatio
描述.This book contains the reviewed, accepted contributions ofthe First International Symposium on Intelligent Computing Systems (ISICS) .that was held in Merida (Mexico) from 16-18 March 2016. .?..We received 25 submissions from 13 countries. Eachsubmission had been evaluated by at least three members of the ProgramCommittee and external reviewers. Based on these reviews, 12 papers wereselected for long oral presentation. In addition to the contributed papers,four keynote speaker presentations were included in the conference program..
出版日期Conference proceedings 2016
關(guān)鍵詞Computing methodologies; artificial Intelligence; neural networks; machine learning; supervised learning
版次1
doihttps://doi.org/10.1007/978-3-319-30447-2
isbn_softcover978-3-319-30446-5
isbn_ebook978-3-319-30447-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Robustness of Deep Convolutional Neural Networks for Image Recognition, adversarial examples and examples perturbed with random noise is measured. Obtained human classification accuracy is compared to the accuracy of deep neural networks measured in the same experimental settings. The results indicate, human performance does not suffer from neural network adversarial noise.
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An Approach to Codification Power on the Behavior of Genetic Algorithms,As have the advantages of no special demand for initial values of decision variables, lower computer storage, and less CPU time for computation. Better results are obtained in comparison the results of traditional Genetic Algorithms. The effectiveness of GAs with crossed binary coding in minimizing the complex function is demonstrated.
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