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008			131125s2013 ne | s |||| 0|eng d
020			_a9789400778696
024	7		_a10.1007/978-94-007-7869-6 _2doi
050		4	_aR858 _b.C384 2013 EB
082	0	4	_a610
100	1		_aCleophas, Ton J. _0Local _1http://viaf.org/viaf/5106724 _986087
245	1	0	_aMachine Learning in Medicine : _bPart Three _cby Ton J. Cleophas, Aeilko H. Zwinderman.
264		1	_aDordrecht, Netherlands _bSpringer _c2013
300			_a1 recurso en línea (XIX, 224 páginas) _b41 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aPreface -- Introduction to Machine Learning Part Three.-Â{u5DAF}olutionary Operations.-Â{uDD6C}ltiple Treatments -- Multiple Endpoints -- Optimal Binning -- Exact P-Values -- Probit Regression -- Over - dispersion.10Â{u286E}dom Effects -- Weighted Least Squares -- Multiple Response Sets -- Complex Samples -- Runs Tests.-Â{u4963}cision Trees -- Spectral Plots -- Newton's Methods -- Stochastic Processes, Stationary Markov Chains -- Stochastic Processes, Absorbing Markov Chains -- Conjoint Models -- Machine Learning and Unsolved Questions -- Index.
520			_aMachine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, 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.
650		7	_aVisión por ordenador _2embne _9159793
700	1		_aZwinderman, Aeilko H. _0Local _0http://id.loc.gov/authorities/names/n00010429 _1http://viaf.org/viaf/69166034 _986088
710	2		_aSpringerLink (Online service) _0Local _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-94-007-7869-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12825232 _b10-10-17 _c01-10-14
942			_2lcc _cLE
945			_aR-RZ EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11554447 _z06-04-17
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