000 | 02443pam a2200373 i 4500 | ||
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003 | AE-DuAU | ||
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_c43253 _d43253 |
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001 | 9783319410623 | ||
005 | 20241127175714.0 | ||
008 | 160822t20162016sz a b 001 0 eng d | ||
020 | _a9783319410623 | ||
020 | _a3319410628 | ||
040 |
_aStDuBDS _beng _cStDuBDS _erda _dUK-RwCLS |
||
050 | 0 | 4 |
_aQ325.5 _b.M87 2016 |
090 | _aQ 325.5 .M87 2016 | ||
100 | 1 |
_aMurty, M. Narasimha, _eauthor. _99413 |
|
245 | 1 | 0 |
_aSupport vector machines and perceptrons : _blearning, optimization, classification, and application to social networks / _cM.N. Murty, Rashmi Raghava. |
264 | 1 |
_aSwitzerland : _bSpringer, _c[2016] |
|
264 | 4 | _c2016 | |
300 |
_axiii, 95 pages : _billustrations (black and white) ; _c24 cm. |
||
336 |
_atext _2rdacontent _btxt |
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336 |
_astill image _2rdacontent _btxt |
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337 |
_aunmediated _2rdamedia _bn |
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338 |
_avolume _2rdacarrier _bnc |
||
490 | 1 |
_aSpringerBriefs in computer science, _x2191-5768 |
|
504 | _aIncludes bibliographical references and index. | ||
505 | 0 | _aIntroduction -- Linear Discriminant Function -- Perceptron -- Linear Support Vector Machines -- Kernel Based SVM -- Application to Social Networks -- Conclusion. | |
520 | 0 | 0 | _aThis 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 |
_aSupport vector machines. _99414 |
|
650 | 0 |
_aSocial networks. _91902 |
|
700 | 1 |
_aRaghava, Rashmi, _eauthor. _99415 |
|
830 | 0 |
_aSpringerBriefs in computer science. _99416 |
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942 |
_2lcc _cBOOK |
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907 | _a43253 |