Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe

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書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018影響因子(影響力)




書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018被引頻次




書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018被引頻次學(xué)科排名




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書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018讀者反饋




書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning – ICANN 2018讀者反饋學(xué)科排名

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0302-9743 Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detecti978-3-030-01423-0978-3-030-01424-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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A RNN-Based Multi-factors Model for Repeat Consumption Prediction behavior, and found that the MF-RNN gets better performance than non-factor RNN. Besides, we analyzed the differences in consumption behaviors between different cities and different regions in China.

脊椎動(dòng)物

Neural Model for the Visual Recognition of Animacy and Social Interactionture. For the generation of training data we propose a novel algorithm that is derived from dynamic human navigation models, and which allows to generate arbitrary numbers of abstract social interaction stimuli by self-organization.

Mendicant

Wolfgang Hoffmann-Riem,Stefan Engelsng problems. In particular, we consider settings of partial observability and leverage the short-term memory capabilities of echo state networks (ESNs) to learn parameterized control policies. Using SPSA, we propose three different variants to adapt the weight matrices of an ESN to the task at hand.

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