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  • 1
    Online-Ressource
    Online-Ressource
    Cham :Springer International Publishing :
    UID:
    almahu_9949301591202882
    Umfang: XIV, 152 p. 8 illus., 6 illus. in color. , online resource.
    Ausgabe: 1st ed. 2022.
    ISBN: 9783030983161
    Serie: Compact Textbooks in Mathematics,
    Inhalt: This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence. Also provided is a detailed description of two biomedical applications of the considered algorithms, demonstrating how close the theory is to being practically implemented. Among the book's several unique features is its analysis of a large class of algorithms of the Learning Theory that essentially comprise every linear regularization scheme, including Tikhonov regularization as a specific case. It also provides a methodology for analyzing not only different supervised learning problems, such as regression or ranking, but also different learning scenarios, such as unsupervised domain adaptation or reinforcement learning. By analyzing these topics using the same theoretical framework, rather than approaching them separately, their presentation is streamlined and made more approachable. An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces is an ideal resource for graduate and postgraduate courses in computational mathematics and data science.
    Anmerkung: Introduction -- Learning in Reproducing Kernel Hilbert Spaces and related integral operators -- Selected topics of the regularization theory -- Regularized learning in RKHS -- Examples of Applications.
    In: Springer Nature eBook
    Weitere Ausg.: Printed edition: ISBN 9783030983154
    Weitere Ausg.: Printed edition: ISBN 9783030983178
    Sprache: Englisch
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    UID:
    gbv_1804280747
    Umfang: 1 Online-Ressource (XIV, 152 Seiten) , Illustrationen
    ISBN: 9783030983161
    Serie: Compact Textbooks in Mathematics
    Inhalt: Introduction -- Learning in Reproducing Kernel Hilbert Spaces and related integral operators -- Selected topics of the regularization theory -- Regularized learning in RKHS -- Examples of Applications.
    Inhalt: This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence. Also provided is a detailed description of two biomedical applications of the considered algorithms, demonstrating how close the theory is to being practically implemented. Among the book’s several unique features is its analysis of a large class of algorithms of the Learning Theory that essentially comprise every linear regularization scheme, including Tikhonov regularization as a specific case. It also provides a methodology for analyzing not only different supervised learning problems, such as regression or ranking, but also different learning scenarios, such as unsupervised domain adaptation or reinforcement learning. By analyzing these topics using the same theoretical framework, rather than approaching them separately, their presentation is streamlined and made more approachable. An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces is an ideal resource for graduate and postgraduate courses in computational mathematics and data science.
    Weitere Ausg.: ISBN 9783030983154
    Weitere Ausg.: ISBN 9783030983178
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 9783030983154
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 9783030983178
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 3
    UID:
    gbv_181340805X
    Umfang: xiv, 152 Seiten , Illustrationen, Diagramme
    ISBN: 9783030983154
    Serie: Compact textbooks in mathematics
    Inhalt: Introduction -- Learning in Reproducing Kernel Hilbert Spaces and related integral operators -- Selected topics of the regularization theory -- Regularized learning in RKHS -- Examples of Applications.
    Inhalt: This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence. Also provided is a detailed description of two biomedical applications of the considered algorithms, demonstrating how close the theory is to being practically implemented. Among the book’s several unique features is its analysis of a large class of algorithms of the Learning Theory that essentially comprise every linear regularization scheme, including Tikhonov regularization as a specific case. It also provides a methodology for analyzing not only different supervised learning problems, such as regression or ranking, but also different learning scenarios, such as unsupervised domain adaptation or reinforcement learning. By analyzing these topics using the same theoretical framework, rather than approaching them separately, their presentation is streamlined and made more approachable. An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces is an ideal resource for graduate and postgraduate courses in computational mathematics and data science.
    Weitere Ausg.: 10.1007/978-3-030-98316-1
    Weitere Ausg.: ISBN 9783030983161
    Weitere Ausg.: Erscheint auch als Online-Ausgabe Pereverzev, Sergej V., 1955 - An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces Cham : Birkhäuser, 2022 ISBN 9783030983161
    Weitere Ausg.: Erscheint auch als Online-Ausgabe Pereverzev, Sergej V., 1955 - An introduction to artificial intelligence based on reproducing kernel Hilbert spaces Cham, Switzerland : Birkhäuser, 2022 ISBN 9783030983161
    Sprache: Englisch
    Fachgebiete: Mathematik
    RVK:
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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