Artificial Neural Networks and Machine Learning -- ICANN 2014
24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings
Published on 18. August 2014
XXV, 852 pages
978-3-319-11179-7 (ISBN)
Description
The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.
The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
XXV, 852 p. 338 illus.
File size
43,40 MB
ISBN-13
978-3-319-11179-7 (9783319111797)
DOI
10.1007/978-3-319-11179-7
Schweitzer Classification
Other editions
Additional editions
Stefan Wermter | Cornelius Weber | Wlodzislaw Duch
Artificial Neural Networks and Machine Learning -- ICANN 2014
24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings
Book
09/2014
Springer
€53.49
Shipment within 10-15 days
Persons
Content
Recurrent Networks.- Sequence Learning.- Echo State Networks.- Recurrent Network Theory.- Competitive Learning and Self-Organisation.- Clustering and Classification.- Trees and Graphs.- Human-Machine Interaction.- Deep Networks.- Theory.- Optimization.- Layered Networks.- Reinforcement Learning and Action.- Vision.- Detection and Recognition.- Invariances and Shape Recovery.- Attention and Pose Estimation.- Supervised Learning.- Ensembles.- Regression.- Classification.- Dynamical Models and Time Series.- Neuroscience.- Cortical Models.- Line Attractors and Neural Fields.- Spiking and Single Cell Models.- Applications.- Users and Social Technologies.- Demonstrations.
