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Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2013; 23rd International C Valeri Mladenov,Petia Koprinkova-Hristova,Nikola K Conf

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發(fā)表于 2025-3-21 18:52:20 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Neural Networks and Machine Learning -- ICANN 2013
期刊簡稱23rd International C
影響因子2023Valeri Mladenov,Petia Koprinkova-Hristova,Nikola K
視頻videohttp://file.papertrans.cn/163/162635/162635.mp4
發(fā)行地址Fast track conference proceedings of ICANN 2013
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2013; 23rd International C Valeri Mladenov,Petia Koprinkova-Hristova,Nikola K Conf
影響因子The book constitutes the proceedings of the 23rd International Conference on Artificial Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The 78 papers included in the proceedings were carefully reviewed and selected from 128 submissions. The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.
Pindex Conference proceedings 2013
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Conference proceedings 2013mber 2013. The 78 papers included in the proceedings were carefully reviewed and selected from 128 submissions. The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformat
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發(fā)表于 2025-3-23 00:24:39 | 只看該作者
Conference proceedings 2013g topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.
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https://doi.org/10.1007/978-3-658-28065-9 the effectiveness of our method, local detection of communities in synthetic benchmark networks and real social networks is examined. The community structure detected by our method is perfectly consistent with the correct community structure of these networks.
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