Support Vector Machines and Perceptrons (Record no. 54306)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02952nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-41063-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421111649.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 160816s2016 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319410630 |
| -- | 978-3-319-41063-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.4 |
| 100 1# - AUTHOR NAME | |
| Author | Murty, M.N. |
| 245 10 - TITLE STATEMENT | |
| Title | Support Vector Machines and Perceptrons |
| Sub Title | Learning, Optimization, Classification, and Application to Social Networks / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 95 p. 25 illus. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Computer Science, |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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.>. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Raghava, Rashmi. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-41063-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2016. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer system failures. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern recognition. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Application software. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern Recognition. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Algorithm Analysis and Problem Complexity. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Appl. in Social and Behavioral Sciences. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | System Performance and Evaluation. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-5768 |
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