TY  - BOOK
AU  - Wermter,Stefan
AU  - Weber,Cornelius
AU  - Duch,Włodzisław
AU  - Honkela,Timo
AU  - Koprinkova-Hristova,Petia
AU  - Magg,Sven
AU  - Palm,Günther
AU  - Villa,Alessandro E.P.
ED  - SpringerLink (Online service)
TI  - Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9783319111797
AV  - Q334-342 
U1  - 006.3 23
PY  - 2014///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Computer science
KW  - Computer software
KW  - Artificial intelligence
KW  - Computer vision
KW  - Optical pattern recognition
KW  - Computer Science
KW  - Artificial Intelligence (incl. Robotics)
KW  - Computation by Abstract Devices
KW  - Algorithm Analysis and Problem Complexity
KW  - Pattern Recognition
KW  - Information Systems Applications (incl. Internet)
KW  - Image Processing and Computer Vision
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-319-11179-7
ER  -