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Ingo Steinwart – författare

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4 produkter

  1. Ingo Steinwart, Andreas Christmann - Support Vector Machines, Inbunden. Tillgänglighet: Lägg i varukorg

    Support Vector Machines

    Av Ingo Steinwart, Andreas Christmann

    Inbunden, 2008

    2591 kr

    Lägg i varukorg

    Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try …

  2. Andreas Christmann, Ingo Steinwart - Support Vector Machines, E-bok. Tillgänglighet: Lägg i varukorg

    Support Vector Machines

    Av Andreas Christmann, Ingo Steinwart

    E-bok, 2008

    2274 kr

    Lägg i varukorg

    Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try …

  3. Ingo Steinwart, Andreas Christmann - Support Vector Machines, Häftad. Tillgänglighet: Lägg i varukorg

    Support Vector Machines

    Av Ingo Steinwart, Andreas Christmann

    Häftad, 2014

    1811 kr

    Lägg i varukorg

    Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try …

  4. Christopher Bülte, Martin Burger, Matthias Hein, Gitta Kutyniok, Sebastian Pokutta, Ingo Steinwart - Theoretical Foundations of Deep Learning, Inbunden. Tillgänglighet: Kommande

    Theoretical Foundations of Deep Learning

    Av Christopher Bülte, Martin Burger m. fl.

    Inbunden, 2026

    2191 kr

    Kommande

    This book offers a comprehensive exploration of the theoretical foundations of deep learning, bridging the gap between its ever increasing empirical success and rigorous mathematical understanding. With a focus on interdisciplinary approaches, it illuminates the interplay between mathematics, …