Latent Variable Analysis and Signal Separation [electronic resource] : 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2-5, 2018, Proceedings / edited by Yannick Deville, Sharon Gannot, Russell Mason, Mark D. Plumbley, Dominic Ward.
Tipo de material: TextoSeries Theoretical Computer Science and General Issues ; 10891Editor: Cham : Springer International Publishing : Imprint: Springer, 2018Edición: 1st ed. 2018Descripción: XVII, 580 p. 150 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783319937649Tema(s): Pattern recognition | Optical data processing | Artificial intelligence | Computer simulation | Numerical analysis | Computer communication systems | Pattern Recognition | Image Processing and Computer Vision | Artificial Intelligence | Simulation and Modeling | Numeric Computing | Computer Communication NetworksFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.4 Clasificación LoC:Q337.5TK7882.P3Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Acceso multiusuario
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.
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.
UABC ; Temporal ; 01/01/2021-12/31/2023.