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Support Vector Machines and Perceptrons by Rashmi Raghava
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Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks
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Description

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.>
Release date Australia
August 25th, 2016
Pages
95
Edition
1st ed. 2016
Audience
  • Professional & Vocational
Illustrations
25 Illustrations, black and white; XIII, 95 p. 25 illus.
Dimensions
155x235x6
ISBN-13
9783319410623
Product ID
25447790

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