Central Library

Normal view MARC view ISBD view

Artificial Neural Networks and Machine Learning -- ICANN 2014 [electronic resource] : 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings / edited by Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa.

Contributor(s): Wermter, Stefan [editor.] | Weber, Cornelius [editor.] | Duch, Wlodzislaw [editor.] | Honkela, Timo [editor.] | Koprinkova-Hristova, Petia [editor.] | Magg, Sven [editor.] | Palm, Günther [editor.] | Villa, Allessandro E.P [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Theoretical Computer Science and General Issues: 8681Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Edition: 1st ed. 2014.Description: XXV, 852 p. 338 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319111797.Subject(s): Artificial intelligence | Computer science | Algorithms | Pattern recognition systems | Application software | Computer vision | Artificial Intelligence | Theory of Computation | Algorithms | Automated Pattern Recognition | Computer and Information Systems Applications | Computer VisionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

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.

There are no comments for this item.

Log in to your account to post a comment.