Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

M.N. Murty, Rashmi Raghava (Autoren)

Buch | Softcover

XIII, 95 Seiten

2016 | 1st ed. 2016
Springer International Publishing (Verlag)
978-3-319-41062-3 (ISBN)

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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.

Dr. M. Narasimha Murty is a professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore.

Introduction.- Linear Discriminant Function.- Perceptron.- Linear Support Vector Machines.- Kernel Based SVM.- Application to Social Networks.- Conclusion.

"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)

Erscheinungsdatum	16.08.2016
Reihe/Serie	SpringerBriefs in Computer Science
Zusatzinfo	XIII, 95 p. 25 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Algorithm analysis and problem complexity • classification • Computer Appl. in Social and Behavioral Sciences • Computer Science • data mining and knowledge discovery • Linear discriminant function • machine learning • Network optimisation • pattern recognition • Support Vector Machine • system performance and evaluation
ISBN-10	3-319-41062-8 / 3319410628
ISBN-13	978-3-319-41062-3 / 9783319410623
Zustand	Neuware