Authors:
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
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.>
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Authors and Affiliations
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Indian Institute of Science, Bangalore, India
M.N. Murty
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IBM India, Bangalore, India
Rashmi Raghava
Bibliographic Information
Book Title: Support Vector Machines and Perceptrons
Book Subtitle: Learning, Optimization, Classification, and Application to Social Networks
Authors: M.N. Murty, Rashmi Raghava
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-41063-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-41062-3Published: 25 August 2016
eBook ISBN: 978-3-319-41063-0Published: 16 August 2016
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 95
Number of Illustrations: 25 b/w illustrations
Topics: Pattern Recognition, Data Mining and Knowledge Discovery, Algorithm Analysis and Problem Complexity, Computer Appl. in Social and Behavioral Sciences, System Performance and Evaluation