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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

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期刊全稱Artificial Neural Networks and Machine Learning – ICANN 2017
期刊簡稱26th International C
影響因子2023Alessandra Lintas,Stefano Rovetta,Alessandro E.P.
視頻videohttp://file.papertrans.cn/163/162639/162639.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P.  Confe
影響因子.The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017...?..The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image Processing and Medical Applications; Advances in Machine Learning.. There are 63 short paper abstracts that are included in the back matter of the volume..
Pindex Conference proceedings 2017
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Shortcut Convolutional Neural Networks for?Classification of Gender and Textureages. However, in tradition they only allow adjacent layers connected, limiting integration of multi-scale information. To further improve their performance in classification, we present a new architecture called shortcut convolutional neural networks. This architecture can concatenate multi-scale f
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Word Embedding Dropout and Variable-Length Convolution Window in Convolutional Neural Network for Set movie databases and e-commerce websites. Convolutional neural network(CNN) has been widely used in sentiment analysis to classify the polarity of reviews. For deep convolutional neural networks, dropout is known to work well in the fully-connected layer. In this paper, we use dropout technique in
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