000			02922nam a22003495i 4500
001			318292
003			MX-SnUAN
005			20160429161123.0
007			cr nn 008mamaa
008			160111s2014 gw | s |||| 0|eng d
020			_a9783319121635 _9978-3-319-12163-5
035			_avtls000419058
039		9	_y201601110910 _zstaff
050		4	_aR-RZ
100	1		_aCleophas, Ton J, _eautor. _9308040
245	1	0	_aMachine learning in medicine - cookbook three / _cTon J. Cleophas, Aeilko H. Zwinderman.
264		1	_aCham : _bSpringer International Publishing : _bSpringer, _c2014.
300			_axiii, 131 páginas : _b37 ilustraciones
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Statistics, _x2191-544X
500			_aSpringer eBooks
505	0		_aPreface -- I. Cluster Models -- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys.- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data.- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships -- II. Linear Models.- Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data.-Generalized Linear Models for Outcome Prediction with Paired Data.- Generalized Linear Models for Predicting Event-Rates.-Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction -- Optimal Scaling of High-sensitivity Analysis of Health Predictors.- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes.- Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread.- Partial Correlations for Removing Interaction Effects from Efficacy Data.- Canonical Regression for Overall Statistics of Multivariate Data -- III. Rules Models -- Neural Networks for Assessing Relationships that are Typically Nonlinear.-Complex Samples Methodologies for Unbiased Sampling.-Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups.- Decision Trees for Decision Analysis.- Multidimensional Scaling for Visualizing Experienced Drug Efficacies.- Stochastic Processes for Long Term Predictions from Short Term Observations.- Optimal Binning for Finding High Risk Cut-offs.- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to be Developed -- Index.
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: _z9783319121628
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-12163-5 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c318292 _d318292