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Support Vector Machines and Perceptrons Learning, Optimization, Classification, and Application to Social Networks /
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 o...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Colección: | SpringerBriefs in Computer Science,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Murty, M.N. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Support Vector Machines and Perceptrons |h [electronic resource] : |b Learning, Optimization, Classification, and Application to Social Networks / |c by M.N. Murty, Rashmi Raghava. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a XIII, 95 p. 25 illus. |b online resource. | ||
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| 490 | 1 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 520 | |a 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.>. | ||
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Social sciences-Data processing. | |
| 650 | 0 | |a Electronic digital computers-Evaluation. | |
| 650 | 1 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Algorithms. |
| 650 | 2 | 4 | |a Computer Application in Social and Behavioral Sciences. |
| 650 | 2 | 4 | |a System Performance and Evaluation. |
| 700 | 1 | |a Raghava, Rashmi. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319410623 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319410647 |
| 830 | 0 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
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