Artificial Neural Networks and Machine Learning – ICANN 2014 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Wermter, Stefan (Επιμελητής έκδοσης), Weber, Cornelius (Επιμελητής έκδοσης), Duch, Włodzisław (Επιμελητής έκδοσης), Honkela, Timo (Επιμελητής έκδοσης), Koprinkova-Hristova, Petia (Επιμελητής έκδοσης), Magg, Sven (Επιμελητής έκδοσης), Palm, Günther (Επιμελητής έκδοσης), Villa, Alessandro E. P. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
• Σειρά: Lecture Notes in Computer Science, 8681
• Θέματα: Computer science.; Computers.; Algorithms.; Artificial intelligence.; Image processing.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computation by Abstract Devices.; Algorithm Analysis and Problem Complexity.; Pattern Recognition.; Information Systems Applications (incl. Internet).; Image Processing and Computer Vision.

 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. 

 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.