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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Support Vector Machines and Perceptrons/ by M.N. Murty, Rashmi Raghava.
Reminder of title:	Learning, Optimization, Classification, and Application to Social Networks /
Author:	Murty, M.N.
other author:	Raghava, Rashmi.
Description:	XIII, 95 p. 25 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Pattern recognition. -
Online resource:	https://doi.org/10.1007/978-3-319-41063-0
ISBN:	9783319410630

Support Vector Machines and Perceptrons = Learning, Optimization, Classification, and Application to Social Networks /
Murty, M.N.

Support Vector Machines and PerceptronsLearning, Optimization, Classification, and Application to Social Networks /[electronic resource] :by M.N. Murty, Rashmi Raghava. - 1st ed. 2016. - XIII, 95 p. 25 illus.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

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.>.

ISBN: 9783319410630

Standard No.: 10.1007/978-3-319-41063-0doiSubjects--Topical Terms:

1253525
Pattern recognition.

LC Class. No.: Q337.5

Dewey Class. No.: 006.4

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