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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Item #: 206073373

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Item #: 206073373

AED 220

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    This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory.
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    Shop An Introduction to Support Vector Machines and Other Kernel-based Learning Methods online at a best price in UAE. 0521780195
    • This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
    Publisher Cambridge University Press
    Publication date March 28, 2000
    Edition 1st
    Language English
    Print length 204 pages
    ISBN-10 0521780195
    ISBN-13 978-0521780193
    Item Weight 1.1 pounds (500 grams)
    Dimensions 7.25 x 0.75 x 10.25 inches (18.4 x 1.9 x 26 cm)

    Who Should Buy?

    Suitable For
    • Data Scientists

      Ideal for data scientists who want to understand SVMs and kernel methods for advanced data analysis and modeling.

    • Machine Learning Students

      Great for students learning machine learning concepts, providing foundational knowledge of SVMs and kernel techniques.

    • AI Researchers

      Useful for researchers exploring sophisticated learning algorithms and seeking to enhance their work with kernel-based methods.

    Not Suitable For
    • Beginners in Programming

      Not suitable for beginners lacking programming or mathematical skills, as it requires understanding of advanced concepts.

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