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Support vector machines and perceptrons : learning, optimization, classification, and application to social networks / M.N. Murty, Rashmi Raghava.
Series: SpringerBriefs in computer sciencePublisher: Switzerland : Springer, [2016]Copyright date: 2016Description: xiii, 95 pages : illustrations (black and white) ; 24 cmContent type:- text
- still image
- unmediated
- volume
- 9783319410623
- 3319410628
- Q325.5 .M87 2016
Contents:
Subject: 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.>.
Introduction --
Linear Discriminant Function --
Perceptron --
Linear Support Vector Machines --
Kernel Based SVM --
Application to Social Networks --
Conclusion.
| Item type | Current library | Home library | Collection | Shelving location | Call number | Status | Barcode | |
|---|---|---|---|---|---|---|---|---|
Books
|
American University in Dubai | American University in Dubai | Non-fiction | Main Collection | Q 325.5 .M87 2016 (Browse shelf(Opens below)) | Available | 5169523 |
Browsing American University in Dubai shelves, Shelving location: Main Collection Close shelf browser (Hides shelf browser)
| Q 325.5 .A28 2012 Learning from data : a short course / | Q 325.5 .A46 2010 Introduction to machine learning / | Q 325.5 .M58 1997 Machine Learning / | Q 325.5 .M87 2016 Support vector machines and perceptrons : learning, optimization, classification, and application to social networks / | Q 325.5 .S45 2018 The deep learning revolution / | Q 327 .M55 1988 Perceptrons : an introduction to computational geometry / | Q 334.7 .C45 2021 The ethics of AI : facts, fictions, and forecasts / |
Includes bibliographical references and index.
Introduction --
Linear Discriminant Function --
Perceptron --
Linear Support Vector Machines --
Kernel Based SVM --
Application to Social Networks --
Conclusion.
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.>.
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