Buch, Englisch, 580 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 896 g
14th International Conference, LVA/ICA 2018, Guildford, UK, July 2-5, 2018, Proceedings
Buch, Englisch, 580 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 896 g
Reihe: Theoretical Computer Science and General Issues
ISBN: 978-3-319-93763-2
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
- Technische Wissenschaften Technik Allgemein Modellierung & Simulation
Weitere Infos & Material
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.
