Support Vector Machines and Perceptrons - Learning, Optimization, Classification, and Application to Social Networks (Paperback, 1st ed. 2016)

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.>

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computer Science
Release date:	August 2016
First published:	2016
Authors:	M.N. Murty • Rashmi Raghava
Dimensions:	235 x 155 x 6mm (L x W x T)
Format:	Paperback
Pages:	95
Edition:	1st ed. 2016
ISBN-13:	978-3-319-41062-3
Categories:	Books > Computing & IT > General theory of computing > Systems analysis & design Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-41062-8
Barcode:	9783319410623

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