Cantitate/Preț
Produs
Total produse
Vezi coșul
Latent Variable Analysis and Signal Separation de Yannick Deville

Latent Variable Analysis and Signal Separation: Lecture Notes in Computer Science, cartea 10891

Editat de Yannick Deville, Sharon Gannot, Russell Mason, Mark D. Plumbley, Dominic Ward
en Limba Engleză Paperback – 6 iun 2018
This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, held in  Guildford, UK, in July 2018.
The 52 full papers  were carefully reviewed and selected from 62 initial submissions. 
As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 33751 lei

Preț vechi: 42189 lei
-20%

Puncte Express: 506

Carte tipărită la comandă

Livrare economică 11-25 august

Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit de la 40000 lei Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783319937632
ISBN-10: 3319937634
Pagini: 600
Ilustrații: XVII, 580 p. 150 illus.
Dimensiuni: 155 x 235 x 33 mm
Greutate: 0.9 kg
Ediția:1st ed. 2018
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Structured Tensor Decompositions and Applications.- Matrix and Tensor Factorizations.- ICA Methods.- Nonlinear Mixtures.- Audio Data and Methods.- Signal Separation Evaluation Campaign.- Deep Learning and Data-driven Methods.- Advances in Phase Retrieval and Applications.- Sparsity-Related Methods.- Biomedical Data and Methods.