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

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期刊全称Artificial Neural Networks and Machine Learning – ICANN 2018
期刊简称27th International C
影响因子2023Věra Kůrková,Yannis Manolopoulos,Ilias Maglogianni
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra Kůrková,Yannis Manolopoulos,Ilias Maglogianni Confe
影响因子.This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27.th. International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018...The papers presented in these volumes was carefully reviewed and selected from  total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detecti
Pindex Conference proceedings 2018
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A Convolutional Neural Network Approach for Modeling Semantic Trajectories and Predicting Future Locions. Current location prediction algorithms go beyond using plain location data and show that additional context information can lead to a higher performance. Moreover, it has been shown that using semantics and projecting GPS trajectories on so called semantic trajectories can further improve the
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Neural Networks for Multi-lingual Multi-label Document Classificational networks for this task with three different configurations. The first one uses static word2vec embeddings that are let as is, while the second one initializes it with word2vec and fine-tunes the embeddings while learning on the available data. The last method initializes embeddings randomly and t
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