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  • 1
    UID:
    (DE-627)1603833366
    Format: [76 S.] , 240 mm x 170 mm
    ISBN: 9783836402415 , 3836402416
    Note: Literaturverz. S. [75] - [76]
    Language: German
    Subjects: Economics
    RVK:
    Keywords: China ; Humanvermögen ; Personalentwicklung
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    (DE-627)675457947
    ISSN: 1923-3965
    In: Journal of management and strategy, Toronto : Sciedu Pr., 2010, 2(2011), 3 vom: Sept., Seite 102-105, 1923-3965
    In: volume:2
    In: year:2011
    In: number:3
    In: month:09
    In: pages:102-105
    Language: English
    Keywords: Aufsatz in Zeitschrift
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  • 3
    Book
    Book
    New York : Dekker
    UID:
    (DE-627)378662511
    Format: IX, 538 S. , zahlr. graph. Darst. , 24 cm
    ISBN: 0824750543
    Series Statement: Electrical and computer engineering 119
    Note: Includes index , Literaturverz. S. 532 - 534
    Language: English
    Subjects: Engineering
    RVK:
    Keywords: Stromrichter ; Bibliografie
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  • 4
    Online Resource
    Online Resource
    Berlin/Boston : De Gruyter
    UID:
    (DE-101)1238696066
    Format: Online-Ressource, 533 Seiten , 50 Illustrationen
    Edition: 1. Auflage
    ISBN: 9783110498073 , 3110498073
    Additional Edition: Erscheint auch als Online-Ausgabe 9783110499506
    Additional Edition: Erscheint auch als Online-Ausgabe 9783110499506
    Additional Edition: Erscheint auch als Druck-Ausgabe 9783110500684
    Additional Edition: Erscheint auch als Druck-Ausgabe 9783110500684
    Language: English
    Keywords: Maschinelles Lernen ; Lie-Gruppe ; Kognitiver Prozess ; Deep learning
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  • 5
    Online Resource
    Online Resource
    Berlin ; Boston : De Gruyter
    UID:
    (DE-604)BV045879557
    Format: 1 online resource (533 Seiten)
    ISBN: 9783110499506
    Content: This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers
    Note: Description based on online resource; title from PDF title page (publisher's Web site, viewed 23. Nov 2018) , In English
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783110500684
    Language: English
    Subjects: Computer Science , Mathematics
    RVK:
    RVK:
    RVK:
    Keywords: Maschinelles Lernen ; Lie-Gruppe ; Kognitiver Prozess ; Deep learning
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 6
    UID:
    (DE-627)1800337981
    ISBN: 9783030518271
    In: Virtual Conference on Usability and User Experience (11. : 2020 : Online), Advances in usability, user experience, wearable and assistive technology, Cham, Switzerland : Springer, 2021, (2021), Seite 272-278, 9783030518271
    In: year:2021
    In: pages:272-278
    Language: English
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  • 7
    Online Resource
    Online Resource
    Berlin : Walter de Gruyter, GmbH
    UID:
    (DE-627)1830958380
    Format: 1 Online-Ressource (xvi, 517 pages) , illustrations
    Content: Lie group machine learning model -- Lie group subspace orbit generation learning -- Symplectic group learning -- Quantum group learning -- Lie group fibre bundle learning -- Lie group covering learning -- Lie group deep structure learning -- Lie group semi-supervised learning -- Lie group kernel learning -- Tensor learning -- Frame bundle connection learning -- Spectral estimation learning -- Finsler geometric learning -- Homology boundary learning -- Category representation learning -- Neuromorphic synergy learning.
    Content: This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artificial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning
    Note: Includes bibliographical references and index , In English
    Additional Edition: 9783110499506
    Additional Edition: 3110499509
    Additional Edition: 9783110498073
    Additional Edition: 3110498073
    Additional Edition: 9783110500684
    Additional Edition: 311050068X
    Additional Edition: 9783110500684
    Additional Edition: Erscheint auch als Druck-Ausgabe Li, Fanzhang Lie group machine learning Berlin ; Boston : Walter de Gruyter, GmbH, [2019]
    Language: English
    Keywords: Electronic books
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  • 8
    Online Resource
    Online Resource
    Berlin/Boston : De Gruyter
    UID:
    (DE-101)1160360960
    Format: Online-Ressource, 337 Seiten
    Edition: 1. Auflage
    ISBN: 9783110518757 , 3110518759
    Note: Lizenzpflichtig
    Additional Edition: Erscheint auch als Druck-Ausgabe 9783110518702
    Additional Edition: Erscheint auch als 9783110520668
    Additional Edition: Erscheint auch als Druck-Ausgabe 9783110518702
    Additional Edition: Erscheint auch als 9783110520668
    Additional Edition: Erscheint auch als Online-Ausgabe 9783110520651
    Additional Edition: Erscheint auch als Online-Ausgabe 9783110520651
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Online Resource
    Online Resource
    Berlin ; Boston : De Gruyter
    UID:
    (DE-605)HT020752957
    Format: 1 online resource (533 p.)
    ISBN: 9783110499506
    Content: This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers
    Additional Edition: 9783110498073
    Additional Edition: 9783110500684
    Language: English
    URL: Cover
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  • 10
    Online Resource
    Online Resource
    Berlin/Boston : De Gruyter
    UID:
    (DE-627)1011475030
    Format: 1 Online-Ressource (338 pages)
    ISBN: 9783110520651
    Content: Intro -- Preface -- Contents -- 1. Dynamic fuzzy machine learning model -- 2. Dynamic fuzzy autonomic learning subspace algorithm -- 3. Dynamic fuzzy decision tree learning -- 4. Concept learning based on dynamic fuzzy sets -- 5. Semi-supervised multi-task learning based on dynamic fuzzy sets -- 6. Dynamic fuzzy hierarchical relationships -- 7. Multi-agent learning model based on dynamic fuzzy logic -- 8. Appendix -- Index
    Additional Edition: 9783110518702
    Additional Edition: Print version Li, Fanzhang Dynamic Fuzzy Machine Learning Berlin/Boston : De Gruyter,c2017 9783110518702
    Language: English
    URL: Volltext  (lizenzpflichtig)
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