000 03511nam a22004695i 4500
001 978-94-007-7869-6
003 DE-He213
005 20140220082945.0
007 cr nn 008mamaa
008 131125s2013 ne | s |||| 0|eng d
020 _a9789400778696
_9978-94-007-7869-6
024 7 _a10.1007/978-94-007-7869-6
_2doi
050 4 _aR-RZ
072 7 _aMBGR
_2bicssc
072 7 _aMED000000
_2bisacsh
082 0 4 _a610
_223
100 1 _aCleophas, Ton J.
_eauthor.
245 1 0 _aMachine Learning in Medicine
_h[electronic resource] :
_bPart Three /
_cby Ton J. Cleophas, Aeilko H. Zwinderman.
264 1 _aDordrecht :
_bSpringer Netherlands :
_bImprint: Springer,
_c2013.
300 _aXIX, 224 p. 41 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- Introduction to Machine Learning Part Three.- Evolutionary Operations.- Multiple Treatments -- Multiple Endpoints -- Optimal Binning -- Exact P-Values -- Probit Regression -- Over - dispersion.10 Random Effects -- Weighted Least Squares -- Multiple Response Sets -- Complex Samples -- Runs Tests.- Decision 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 0 _aMedicine.
650 0 _aComputer vision.
650 0 _aStatistics.
650 1 4 _aBiomedicine.
650 2 4 _aBiomedicine general.
650 2 4 _aMedicine/Public Health, general.
650 2 4 _aStatistics, general.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
700 1 _aZwinderman, Aeilko H.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789400778689
856 4 0 _uhttp://dx.doi.org/10.1007/978-94-007-7869-6
912 _aZDB-2-SBL
999 _c100027
_d100027