Artificial Neural Networks and Machine Learning - ICANN 2017, 1st ed. 2017 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part I Theoretical Computer Science and General Issues Series
Coordonnateurs : Lintas Alessandra, Rovetta Stefano, Verschure Paul F.M.J., Villa Alessandro E.P.
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.
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.
Includes supplementary material: sn.pub/extras
Date de parution : 10-2017
Ouvrage de 469 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 52,74 €
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Mots-clés :
artificial intelligence; bio-embedded electronics; classification and regression trees; computational complexity and cryptography; computer vision; computing methodologies; formal languages and automata theory; kernel methods; learning algorithms; learning in probabilistic graphical models; machine learning algorithms; machine learning approaches; network dynamics; network structure; neural networks; recurrent neural networks; robotics; signal processing
