Title: Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (SpringerBriefs in Computer Science)
Publisher: Springer
Publication Date: 2016
Language: English
ISBN 10: 3319410628
ISBN 13: 9783319410623
Binding: Soft cover
Condition: New

About this title

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.>

Review

“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)

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