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

By: Contributor(s): Material type: TextTextPublication details: New Delhi Cambridge 2001Description: 189pISBN:
  • 0521780195
Subject(s): DDC classification:
  • 006.31 CRI-I
Summary: 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.
Item type: Books and Monographs
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Item type Current library Home library Call number Materials specified Status Date due Barcode
Books and Monographs Central Library, NIT Jalandhar General Stacks Central Library, NIT Jalandhar 006.31 CRI-I (Browse shelf(Opens below)) Available 88500
Books and Monographs Central Library, NIT Jalandhar General Stacks Central Library, NIT Jalandhar 006.31 CRI-I (Browse shelf(Opens below)) Available 88501

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.

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