SpringerBriefs in Computer Science

Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

Authors: Murty, M.N., Raghava, Rashmi

Free Preview
  • Presents a review of linear classifiers, with a focus on those based on linear discriminant functions
  • Discusses the application of support vector machines (SVMs) in link prediction in social networks
  • Describes the perceptron, another popular linear classifier, and compares its performance with that of the SVM in different application areas
see more benefits

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-3-319-41063-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-319-41062-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.>

Reviews

“The book deals primarily with classification, focused on linear classifiers. … It is intended to senior undergraduate and graduate students and researchers working in machine learning, data mining and pattern recognition.” (Smaranda Belciug, zbMATH 1365.68003, 2017) 


Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-3-319-41063-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-319-41062-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Support Vector Machines and Perceptrons
Book Subtitle
Learning, Optimization, Classification, and Application to Social Networks
Authors
Series Title
SpringerBriefs in Computer Science
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-41063-0
DOI
10.1007/978-3-319-41063-0
Softcover ISBN
978-3-319-41062-3
Series ISSN
2191-5768
Edition Number
1
Number of Pages
XIII, 95
Number of Illustrations
25 b/w illustrations
Topics