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Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (SpringerBriefs in Computer Science) - Tapa blanda

Libro 209 de 322: SpringerBriefs in Computer Science

Murty, M.N.; Raghava, Rashmi

 
9783319410623: Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks (SpringerBriefs in Computer Science)
Tapa blanda
ISBN 10: 3319410628 ISBN 13: 9783319410623
Editorial: Springer, 2016

Sinopsis

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.>

"Sinopsis" puede pertenecer a otra edición de este libro.

Críticas

“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) 

Reseña del editor

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.>

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2016
Idioma
Inglés
ISBN 10 
3319410628
ISBN 13 
9783319410623
Encuadernación
Tapa blanda
Número de páginas
112
Contacto del fabricante
no disponible
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