Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Condizione: New.
Lingua: Inglese
Editore: Now Publishers Inc 2016-12-19, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Da: Chiron Media, Wallingford, Regno Unito
EUR 96,30
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: Wegmann1855, Zwiesel, Germania
EUR 92,00
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 116,50
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 196.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 130,97
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Da: Revaluation Books, Exeter, Regno Unito
EUR 146,89
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Aggiungi al carrelloPaperback. Condizione: Brand New. 196 pages. 9.21x6.14x0.42 inches. In Stock.
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Tensor Networks for Dimensionality Reduction and Large-Scale Optimization | Part 1 Low-Rank Tensor Decompositions | Andrzej Cichocki (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2016 | Now Publishers | EAN 9781680832228 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 117,08
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 273,34
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Da: Majestic Books, Hounslow, Regno Unito
EUR 147,32
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 153,95
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 196.