Cantitate/Preț
Produs

Artificial Neural Networks and Machine Learning -- ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014, Proceedings: Lecture Notes in Computer Science, cartea 8681

Editat de Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa
en Limba Engleză Paperback – 22 sep 2014
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.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 36112 lei

Preț vechi: 45140 lei
-20% Nou

Puncte Express: 542

Preț estimativ în valută:
6910 7234$ 5718£

Carte tipărită la comandă

Livrare economică 05-19 aprilie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319111780
ISBN-10: 3319111787
Pagini: 880
Ilustrații: XXV, 852 p. 338 illus.
Dimensiuni: 155 x 235 x 46 mm
Greutate: 1.21 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.