Artificial Neural Networks and Machine Learning -- ICANN 2014 : 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.

Format:

Book

Contributor:

Wermter, Stefan, editor.

Weber, Cornelius, editor.

Duch, W. (Włodzisław), 1954- editor.

Honkela, Timo, editor.

Koprinkova-Hristova, Petia, editor.

Magg, Sven, editor.

Palm, Günther, editor.

Villa, Allessandro E.P., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 8681.

Theoretical Computer Science and General Issues ; 8681

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Algorithms.

Pattern perception.

Application software.

Optical data processing.

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Pattern Recognition.

Information Systems Applications (incl. Internet).

Image Processing and Computer Vision.

Local Subjects:

Artificial Intelligence.

Computation by Abstract Devices.

Algorithm Analysis and Problem Complexity.

Pattern Recognition.

Information Systems Applications (incl. Internet).

Image Processing and Computer Vision.

Physical Description:

1 online resource (XXV, 852 pages) : 338 illustrations.

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2014.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-11179-7

9783319111797

Access Restriction:

Restricted for use by site license.