Kernel Methods and Machine Learning
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Kernel Methods and Machine Learning  (English, Hardcover, Kung S. Y.)

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    Highlights
    • Language: English
    • Binding: Hardcover
    • Publisher: Cambridge University Press
    • Genre: Computers
    • ISBN: 9781107024960, 9781107024960
    • Pages: 572
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  • Description
    Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.
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    Book Details
    Imprint
    • Cambridge University Press
    Dimensions
    Width
    • 29 mm
    Height
    • 252 mm
    Length
    • 176 mm
    Weight
    • 1350 gr
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