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  3. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
  • Cited by 7155
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    • Publisher:
      Cambridge University Press
      Publication date:
      March 2013
      March 2000
      ISBN:
      9780511801389
      9780521780193
      DOI:
      https://doi.org/10.1017/CBO9780511801389
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.5kg, 204 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Computer Science, Pattern Recognition and Machine Learning, Computational Biology and Bioinformatics
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    • Selected: Digital
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    Subjects:
    Computer Science, Pattern Recognition and Machine Learning, Computational Biology and Bioinformatics
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    Book description

    This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. 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 ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

    Reviews

    ‘… the most accessible introduction to the area I have yet seen’.

    D. J. Hand Source: Publication of the International Statistical Institute

    ‘The book is an admirable presentation of this powerful new approach to pattern classification.’

    Alex M. Andrew Source: Robotica

    ' … an excellent book, complete and readable without big requirements in mathematical functional analysis.'

    Source: Zentralblatt für Mathematik und ihre Grenzgebiete Mathematics Abstracts

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