LEADER 04544nam 22007095 450 001 9910437846303321 005 20200706044429.0 010 $a94-007-7869-4 024 7 $a10.1007/978-94-007-7869-6 035 $a(CKB)3710000000031343 035 $a(SSID)ssj0001066464 035 $a(PQKBManifestationID)11634429 035 $a(PQKBTitleCode)TC0001066464 035 $a(PQKBWorkID)11067236 035 $a(PQKB)10889075 035 $a(DE-He213)978-94-007-7869-6 035 $a(MiAaPQ)EBC6314936 035 $a(MiAaPQ)EBC1591862 035 $a(Au-PeEL)EBL1591862 035 $a(CaPaEBR)ebr10983435 035 $a(OCoLC)828302727 035 $a(PPN)176130365 035 $a(EXLCZ)993710000000031343 100 $a20131125d2013 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medicine $ePart Three /$fby Ton J. Cleophas, Aeilko H. Zwinderman 205 $a1st ed. 2013. 210 1$aDordrecht :$cSpringer Netherlands :$cImprint: Springer,$d2013. 215 $a1 online resource (XIX, 224 p. 41 illus.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a94-007-7868-6 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aMedicine 606 $aStatistics  606 $aOptical data processing 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aMedicine/Public Health, general$3https://scigraph.springernature.com/ontologies/product-market-codes/H00007 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 615 0$aMedicine. 615 0$aStatistics . 615 0$aOptical data processing. 615 14$aBiomedicine, general. 615 24$aMedicine/Public Health, general. 615 24$aStatistics, general. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 676 $a610.28563 700 $aCleophas$b Ton J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0472359 702 $aZwinderman$b Aeilko H$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437846303321 996 $aMachine Learning in Medicine$92507524 997 $aUNINA