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Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications - Tapa dura
Sinopsis
This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including
- blind source separation;
- social network mining;
- image and video processing;
- array signal processing; and,
- wireless communications.
The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.
Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
"Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including
- blind source separation;
- social network mining;
- image and video processing;
- array signal processing; and,
- wireless communications.
The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.
Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialSpringer-Verlag GmbH
- Año de publicación2023
- ISBN 10 303122437X
- ISBN 13 9783031224379
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas196
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning
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ISBN 10: 303122437X
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applicatio. Nº de ref. del artículo: 748634232
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning
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ISBN 10: 303122437X
ISBN 13: 9783031224379
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, includingblind source separation;social network mining;image and video processing;array signal processing; and,wireless communications.The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods. 196 pp. Englisch. Nº de ref. del artículo: 9783031224379
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning : Modeling, Tuning-Free Algorithms, and Applications
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ISBN 10: 303122437X
ISBN 13: 9783031224379
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, includingblind source separation;social network mining;image and video processing;array signal processing; and,wireless communications.The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods. Nº de ref. del artículo: 9783031224379
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications
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Bayesian Tensor Decomposition for Signal Processing and Machine Learning
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ISBN 10: 303122437X
ISBN 13: 9783031224379
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Buch. Condición: Neu. Neuware -This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, includingSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 196 pp. Englisch. Nº de ref. del artículo: 9783031224379
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