000			03467nam a22003735i 4500
001			313647
003			MX-SnUAN
005			20160429160708.0
007			cr nn 008mamaa
008			150903s2013 ne | o |||| 0|eng d
020			_a9789400768864 _99789400768864
024	7		_a10.1007/9789400768864 _2doi
035			_avtls000367978
039		9	_a201509030719 _bVLOAD _c201405070450 _dVLOAD _y201402251641 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aR-RZ
100	1		_aCleophas, Ton J. _eautor _9308040
245	1	0	_aMachine Learning in Medicine : _bPart Two / _cby Ton J. Cleophas, Aeilko H. Zwinderman.
264		1	_aDordrecht : _bSpringer Netherlands : _bImprint: Springer, _c2013.
300			_axiv, 231 páginas 47 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aIntroduction to Machine Learning Part Two -- Two-stage Least Squares -- Multiple Imputations -- Bhattacharya Analysis -- Quality-of-life (QOL) Assessments with Odds Ratios -- Logistic Regression for Assessing Novel Diagnostic Tests against Control -- Validating Surrogate Endpoints -- Two-dimensional Clustering -- Multidimensional Clustering -- Anomaly Detection -- Association Rule Analysis -- Multidimensional Scaling -- Correspondence Analysis -- Multivariate Analysis of Time Series -- Support Vector Machines -- Bayesian Networks -- Protein and DNA Sequence Mining -- Continuous Sequential Techniques -- Discrete Wavelet Analysis -- Machine Learning and Common Sense -- Statistical Tables -- Index.
520			_aMachine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aZwinderman, Aeilko H. _eautor _9308041
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9789400768857
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-94-007-6886-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c313647 _d313647