000			03061nam a22005295i 4500
001			978-1-4614-5323-9
003			DE-He213
005			20200421112038.0
007			cr nn 008mamaa
008			121026s2013 xxu| s |||| 0|eng d
020			_a9781461453239 _9978-1-4614-5323-9
024	7		_a10.1007/978-1-4614-5323-9 _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		_aDougherty, Geoff. _eauthor.
245	1	0	_aPattern Recognition and Classification _h[electronic resource] : _bAn Introduction / _cby Geoff Dougherty.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXI, 196 p. 158 illus., 104 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Classification -- Nonmetric Methods -- Statistical Pattern Recognition -- Supervised Learning -- Nonparametric Learning -- Feature Extraction and Selection -- Unsupervised Learning -- Estimating and Comparing Classifiers -- Projects.
520			_aThe use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
650		0	_aComputer science.
650		0	_aPattern recognition.
650		0	_aBioinformatics.
650		0	_aComputational biology.
650		0	_aAlgorithms.
650		0	_aStatistical physics.
650	1	4	_aComputer Science.
650	2	4	_aPattern Recognition.
650	2	4	_aNonlinear Dynamics.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aComputer Appl. in Life Sciences.
650	2	4	_aAlgorithms.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461453222
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-5323-9
912			_aZDB-2-SCS
942			_cEBK
999			_c56453 _d56453