000			02162nam a22003498i 4500
001			CR9780511812651
003			UkCbUP
005			20200124160256.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101021s1996||||enk o ||1 0|eng|d
020			_a9780511812651 (ebook)
020			_z9780521460866 (hardback)
020			_z9780521717700 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.87 _b.R56 1996
082	0	0	_a006.4 _220
100	1		_aRipley, Brian D., _d1952- _eauthor.
245	1	0	_aPattern recognition and neural networks / _cB.D. Ripley.
246	3		_aPattern Recognition & Neural Networks
264		1	_aCambridge : _bCambridge University Press, _c1996.
300			_a1 online resource (xi, 403 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aThis 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.
650		0	_aNeural networks (Computer science)
650		0	_aPattern recognition systems.
776	0	8	_iPrint version: _z9780521460866
856	4	0	_uhttps://doi.org/10.1017/CBO9780511812651
999			_c519914 _d519912