Artificial Neural Networks and Machine Learning -- ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings
Author:   Stefan Wermter ,  Cornelius Weber ,  Wlodzislaw Duch ,  Timo Honkela
Publisher:   Springer International Publishing AG
Edition:   2014 ed.
Volume:   8681
ISBN:  

9783319111780


Pages:   852
Publication Date:   22 September 2014
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Artificial Neural Networks and Machine Learning -- ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings


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Overview

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.

Full Product Details

Author:   Stefan Wermter ,  Cornelius Weber ,  Wlodzislaw Duch ,  Timo Honkela
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2014 ed.
Volume:   8681
Dimensions:   Width: 15.50cm , Height: 4.40cm , Length: 23.50cm
Weight:   1.324kg
ISBN:  

9783319111780


ISBN 10:   3319111787
Pages:   852
Publication Date:   22 September 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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