000			03708cam a2200373 i 4500
001			on1221016186
003			OCoLC
005			20240726104827.0
008			201015s2021 nyuab ob 001 0 eng
010			_a2020047111
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dYDX _dOCLCO _dNT
020			_a9781536188653 _q((electronic)l(electronic)ctronic)
042			_apcc
050	0	4	_aQA325 _b.S877 2021
049			_aMAIN
245	1	0	_aSupport vector machines : _bevolution and applications / _cPooja Saigal, editor.
300			_a1 online resource (xii, 233 pages) : _billustrations (some color), color maps
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
490	1		_aComputer science, technology and applications
504			_a2
505	0	0	_aIntroduction to support vector machines / _rPooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India -- _tJourney of support vector machines : from maximum-margin hyperplane to a pair of non-parallel hyperplanes / _rPooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India -- _tPower spectrum entropy-based support vector machine for quantitative diagnosis of rotor vibration process faults / _rCheng-Wei Fei, Department of Aeronautics and Astronautics, Fudan University, Shanghai, China.
520	0		_a"Support Vector Machines: Evolution and Applications reviews the basics of Support Vector Machines (SVM), their evolution and applications in diverse fields. SVM is an efficient supervised learning approach popularly used for pattern recognition, medical image classification, face recognition and various other applications. In the last 25 years, a lot of research has been carried out to extend the use of SVM to a variety of domains. This book is an attempt to present the description of a conventional SVM, along with discussion of its different versions and recent application areas. The first chapter of this book introduces SVM and presents the optimization problems for a conventional SVM. Another chapter discusses the journey of SVM over a period of more than two decades. SVM is proposed as a separating hyperplane classifier that partitions the data belonging to two classes. Later on, various versions of SVM are proposed that obtain two hyperplanes instead of one. A few of these variants of SVM are discussed in this book. The major part of this book discusses some interesting applications of SVM in areas like quantitative diagnosis of rotor vibration process faults through power spectrum entropy-based SVM, hardware architectures of SVM applied in pattern recognition systems, speaker recognition using SVM, classification of iron ore in mines and simultaneous prediction of the density and viscosity for the ternary system water- ethanol-ethylene glycol ionic liquids. The latter part of the book is dedicated to various approaches for the extension of SVM and similar classifiers to a multi-category framework, so that they can be used for the classification of data with more than two classes"-- _cProvided by publisher.
530			_a2 _ub
650		0	_aSupport vector machines.
655		1	_aElectronic Books.
700	1		_aSaigal, Pooja, _e5
856	4	0	_zClick to access digital title | log in using your CIU ID number and my.ciu.edu password. _uhttpss://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2608287&site=eds-live&custid=s3260518
942			_cOB _D _eEB _hQA. _m2021 _QOL _R _x _8NFIC _2LOC
994			_a92 _bNT
999			_c79645 _d79645
902			_a1 _bCynthia Snell _c1 _dCynthia Snell