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OCoLC |
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20231120010614.0 |
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211101s2021 xx o ||| 0 eng d |
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|a 1283495620
|a 1284778571
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|a 012824447X
|q (Paper)
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|a 9780128244470
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|a 9780323859653
|q (Proquest Ebook Central)
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|a 0323859658
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|z (OCoLC)1283495620
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|a QA76.9.M35
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|a 004
|2 23
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|a Liu, Yipeng.
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|a Tensors for Data Processing
|h [ressource� lectronique] :
|b Theory,Methods, and Applications.
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260 |
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|a San Diego, UNITED STATES
|b Academic Press
|c 2021.
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300 |
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|a 1 online resource (1 ressource en ligne (598))
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336 |
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|a texte
|b txt
|2 rdacontent
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337 |
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|a informatique
|b c
|2 rdamedia
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338 |
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|a ressource en ligne
|b cr
|2 rdacarrier
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588 |
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|a Description ba�se sur la version papier.
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|a Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This reference is ideal for students, researchers and industry developers who want to understand and use tensor-based data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear data structure loss that occurs in classical matrix-based data processing methods. This move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics and quantum chemistry.
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650 |
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|a Tensor algebra
|x Data processing.
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650 |
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6 |
|a Alg�ebre tensorielle
|0 (CaQQLa)201-0219788
|x Informatique.
|0 (CaQQLa)201-0380011
|
655 |
|
7 |
|a e-books.
|2 aat
|0 (CStmoGRI)aatgf300265554
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655 |
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|a Livres num�eriques.
|2 rvmgf
|0 (CaQQLa)RVMGF-000000267
|
856 |
4 |
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|u https://sciencedirect.uam.elogim.com/science/book/9780128244470
|z Texto completo
|