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Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

  • M.N. Murty
  • Rashmi Raghava

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. M. N. Murty, Rashmi Raghava
    Pages 1-14
  3. M. N. Murty, Rashmi Raghava
    Pages 15-25
  4. M. N. Murty, Rashmi Raghava
    Pages 27-40
  5. M. N. Murty, Rashmi Raghava
    Pages 41-56
  6. M. N. Murty, Rashmi Raghava
    Pages 57-67
  7. M. N. Murty, Rashmi Raghava
    Pages 69-83
  8. M. N. Murty, Rashmi Raghava
    Pages 85-87
  9. Back Matter
    Pages 89-95

About this book

Introduction

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

Keywords

Support vector machine Linear discriminant function Machine learning Network optimisation Classification

Authors and affiliations

  • M.N. Murty
    • 1
  • Rashmi Raghava
    • 2
  1. 1.Indian Institute of ScienceBangaloreIndia
  2. 2.IBM IndiaBangaloreIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-41063-0
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-41062-3
  • Online ISBN 978-3-319-41063-0
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site