Support Vector Machines and Perceptrons [electronic resource] : Learning, Optimization, Classification, and Application to Social Networks / by M.N. Murty, Rashmi Raghava.
By: Murty, M.N [author.].
Contributor(s): Raghava, Rashmi [author.] | SpringerLink (Online service).
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BookSeries: SpringerBriefs in Computer Science: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIII, 95 p. 25 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319410630.Subject(s): Computer science | Computer system failures | Algorithms | Data mining | Pattern recognition | Application software | Computer Science | Pattern Recognition | Data Mining and Knowledge Discovery | Algorithm Analysis and Problem Complexity | Computer Appl. in Social and Behavioral Sciences | System Performance and EvaluationAdditional physical formats: Printed edition:: No titleDDC classification: 006.4 Online resources: Click here to access online
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Springer eBooksSummary: 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.>.
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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