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Titlebook: Neural Information Processing; 21st International C Chu Kiong Loo,Keem Siah Yap,Kaizhu Huang Conference proceedings 2014 Springer Internati

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發(fā)表于 2025-3-21 19:10:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Information Processing
副標題21st International C
編輯Chu Kiong Loo,Keem Siah Yap,Kaizhu Huang
視頻videohttp://file.papertrans.cn/664/663591/663591.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Neural Information Processing; 21st International C Chu Kiong Loo,Keem Siah Yap,Kaizhu Huang Conference proceedings 2014 Springer Internati
描述The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The selected papers cover major topics of theoretical research, empirical study, and applications of neural information processing research. The 3 volumes represent topical sections containing articles on cognitive science, neural networks and learning systems, theory and design, applications, kernel and statistical methods, evolutionary computation and hybrid intelligent systems, signal and image processing, and special sessions intelligent systems for supporting decision, making processes, theories and applications, cognitive robotics, and learning systems for social network and web mining.
出版日期Conference proceedings 2014
關(guān)鍵詞activity recognition; artificial intelligence; big data; bio-inspired computing; brain-computer interfac
版次1
doihttps://doi.org/10.1007/978-3-319-12643-2
isbn_softcover978-3-319-12642-5
isbn_ebook978-3-319-12643-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Ko Sakai,Ken Kurematsu,Shouhei Matsuokaand the community will also grow. I don‘t see any real evidence supporting this view. Also O&H show some doubts, when they state that “it will be interesting=8A to see whether interoperability difficulties appear and accelerates the drift [in the direction of increasingly different coding languages
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Wei Zhang,Jie Yang,Wenjing Jia,Nikola Kasabov,Zhenhong Jia,Lei Zhou
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Classification of Fish Ectoparasite Genus , SEM Images Using ASM and Complex Network Modelng the initial classification of . species, with an average of 98.36%. Using MLP classifier, only one species has been misallocated. It is essential, therefore, to employ a method that does not generate type I or type II misclassifications where . is concerned. In comparison, only K-NN classifier ha
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