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Titlebook: Artificial Neural Networks - ICANN 2008; 18th International C Véra Kůrková,Roman Neruda,Jan Koutník Conference proceedings 2008 Springer-Ve

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发表于 2025-3-21 17:18:25 | 显示全部楼层 |阅读模式
期刊全称Artificial Neural Networks - ICANN 2008
期刊简称18th International C
影响因子2023Véra Kůrková,Roman Neruda,Jan Koutník
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Neural Networks - ICANN 2008; 18th International C Véra Kůrková,Roman Neruda,Jan Koutník Conference proceedings 2008 Springer-Ve
影响因子This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The second volume is devoted to pattern recognition and data analysis, hardware and embedded systems, computational neuroscience, connectionistic cognitive science, neuroinformatics and neural dynamics. it also contains papers from two special sessions coupling, synchronies, and firing patterns: from cognition to disease, and constructive neural networks and two workshops new trends in self-organization and optimization of artificial neural networks, and adaptive mechanisms of the perception-action cycle.
Pindex Conference proceedings 2008
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Fennoscandian Tundra Ecosystems9% of the moves made in test expert Go games, improving upon the state of the art, and that the best single convolutional neural network of the ensemble achieves 34% accuracy. This network has less than 10. parameters.
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0302-9743 etworks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The second volume is devoted to pattern recognition and data analysis, hardware and embedded systems, computational neuros
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Learning Similarity Measures from Pairwise Constraints with Neural Networksf a small set of supervised examples is used for training. The approximation capabilities of the proposed model are also investigated. Moreover, the experiments carried out on some benchmark datasets show that SNNs almost always outperform other similarity learning methods proposed in the literature.
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Associative Memories Applied to Pattern Recognitiontern recognition problems. In this paper we gather different results provided by a dynamic associative model and present new results in order to describe how this model can be applied to solve different complex problems in pattern recognition such as object recognition, image restoration, occluded object recognition and voice recognition.
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