Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks

✍ Scribed by M.N. Murty, Rashmi Raghava

Publisher: Springer International Publishing
Year: 2016
Tongue: English
Leaves: 103
Series: SpringerBriefs in Computer Science
Edition: 1st ed.
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.>

✦ Table of Contents

Front Matter....Pages i-xiii
Introduction....Pages 1-14
Linear Discriminant Function....Pages 15-25
Perceptron....Pages 27-40
Linear Support Vector Machines....Pages 41-56
Kernel-Based SVM....Pages 57-67
Application to Social Networks....Pages 69-83
Conclusion....Pages 85-87
Back Matter....Pages 89-95

✦ Subjects

Computer science;Informatique;Computer system failures;Pannes système (Informatique);Algorithms;Algorithmes;Data mining;Exploration de données (Informatique);Pattern perception;Application software;Logiciels d'application

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