LEADER 01338oam 2200325z- 450 001 9910164043903321 005 20230913112557.0 010 $a1-62855-934-9 035 $a(CKB)3710000001056142 035 $a(BIP)056966106 035 $a(EXLCZ)993710000001056142 100 $a20190224c2017uuuu -u- - 101 0 $aeng 200 10$aContando los cangrejos herradura a la luz de la luna 210 $cArbordale Publishing 215 $a1 online resource (32 p.) $cill 311 $a1-62855-932-2 330 8 $aĦTambie?n los nin?os pueden involucrarse en la ciencia! En esta colaboracio?n entre la Dra. Ecologista Neeti Bathala y Jennifer Keats Curtis, conoceremos a Lana y a su mama?. Las dos son voluntarias cada verano para contar los cangrejos herradura que visitan su playa. Los lectores van a aprender datos muy valiosos acerca de esos animales antiguos y co?mo pueden involucrarse en el esfuerzo para conservarlos. 610 $aCounting 610 $aCrabs 610 $aCounting Books 610 $aJuvenile Nonfiction 676 $a595.3/87 700 $aBathala$b Neeti$f1968-$01436515 702 $aCurtis$b Jennifer Keats 702 $aV. Jones$b Veronica$4ill 906 $aBOOK 912 $a9910164043903321 996 $aContando los cangrejos herradura a la luz de la luna$93595377 997 $aUNINA LEADER 04965nam 22008295 450 001 9910437826503321 005 20200707003733.0 010 $a94-007-6886-9 024 7 $a10.1007/978-94-007-6886-4 035 $a(CKB)3390000000037221 035 $a(EBL)1317207 035 $a(OCoLC)847514802 035 $a(SSID)ssj0000894412 035 $a(PQKBManifestationID)11515290 035 $a(PQKBTitleCode)TC0000894412 035 $a(PQKBWorkID)10840860 035 $a(PQKB)10525664 035 $a(DE-He213)978-94-007-6886-4 035 $a(MiAaPQ)EBC6315188 035 $a(MiAaPQ)EBC1317207 035 $a(Au-PeEL)EBL1317207 035 $a(CaPaEBR)ebr10968950 035 $a(PPN)170495256 035 $a(EXLCZ)993390000000037221 100 $a20130531d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medicine $ePart Two /$fby Ton J. Cleophas, Aeilko H. Zwinderman 205 $a1st ed. 2013. 210 1$aDordrecht :$cSpringer Netherlands :$cImprint: Springer,$d2013. 215 $a1 online resource (234 p.) 300 $aDescription based upon print version of record. 311 $a94-007-9512-2 311 $a94-007-6885-0 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aMedicine 606 $aEntomology 606 $aStatistics 606 $aOptical data processing 606 $aLiteracy 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aEntomology$3https://scigraph.springernature.com/ontologies/product-market-codes/L25090 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 606 $aLiteracy$3https://scigraph.springernature.com/ontologies/product-market-codes/O40000 615 0$aMedicine. 615 0$aEntomology. 615 0$aStatistics. 615 0$aOptical data processing. 615 0$aLiteracy. 615 14$aBiomedicine, general. 615 24$aEntomology. 615 24$aMedicine/Public Health, general. 615 24$aStatistics, general. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aLiteracy. 676 $a610.285 700 $aCleophas$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 $a9910437826503321 996 $aMachine Learning in Medicine$92507524 997 $aUNINA