000			02952nam a22005535i 4500
001			978-3-319-41063-0
003			DE-He213
005			20200421111649.0
007			cr nn 008mamaa
008			160816s2016 gw | s |||| 0|eng d
020			_a9783319410630 _9978-3-319-41063-0
024	7		_a10.1007/978-3-319-41063-0 _2doi
050		4	_aQ337.5
050		4	_aTK7882.P3
072		7	_aUYQP _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.4 _223
100	1		_aMurty, M.N. _eauthor.
245	1	0	_aSupport Vector Machines and Perceptrons _h[electronic resource] : _bLearning, Optimization, Classification, and Application to Social Networks / _cby M.N. Murty, Rashmi Raghava.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aXIII, 95 p. 25 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
520			_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	_aComputer science.
650		0	_aComputer system failures.
650		0	_aAlgorithms.
650		0	_aData mining.
650		0	_aPattern recognition.
650		0	_aApplication software.
650	1	4	_aComputer Science.
650	2	4	_aPattern Recognition.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aComputer Appl. in Social and Behavioral Sciences.
650	2	4	_aSystem Performance and Evaluation.
700	1		_aRaghava, Rashmi. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319410623
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-41063-0
912			_aZDB-2-SCS
942			_cEBK
999			_c54306 _d54306