LEADER 01180nam0-22003371i-450- 001 990001863830403321 005 20081020143212.0 035 $a000186383 035 $aFED01000186383 035 $a(Aleph)000186383FED01 035 $a000186383 100 $a20030910d1963----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $a<>impiego di un derivato isobutirrico del metiltiouracile nell'alimentazione del pollo da carne$fAlberto Bonomi, Gian Luigi Quaglio 210 $a[S.l.$cs.n.]$d1963 215 $a17 p.$d24 cm 225 1 $aRaccolta di memorie$fUniversità degli studi di Torino. Facoltà di scienze agrarie$v372 300 $aEstr. da: Rivista di zootecnica agricoltura veterinaria, n. 10,1963 610 0 $aAlimentazione animale 676 $a636.084 700 1$aBonomi,$bAlberto$081798 701 1$aQuaglio,$bGian Luigi$0358409 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001863830403321 952 $a60 MISC. B 131/372$fFAGBC 959 $aFAGBC 996 $aImpiego di un derivato isobutirrico del metiltiouracile nell'alimentazione del pollo da carne$9401448 997 $aUNINA LEADER 03445nam 22007212 450 001 9910825946103321 005 20160211123446.0 010 $a1-107-21880-2 010 $a0-511-99432-X 010 $a1-282-97834-9 010 $a9786612978340 010 $a0-511-97582-1 010 $a0-511-99209-2 010 $a0-511-99312-9 010 $a0-511-98930-X 010 $a0-511-98752-8 010 $a0-511-99111-8 035 $a(CKB)2670000000069769 035 $a(EBL)647360 035 $a(OCoLC)700706144 035 $a(SSID)ssj0000473438 035 $a(PQKBManifestationID)11331151 035 $a(PQKBTitleCode)TC0000473438 035 $a(PQKBWorkID)10437705 035 $a(PQKB)10594099 035 $a(UkCbUP)CR9780511975820 035 $a(Au-PeEL)EBL647360 035 $a(CaPaEBR)ebr10442834 035 $a(CaONFJC)MIL297834 035 $a(MiAaPQ)EBC647360 035 $a(PPN)261192973 035 $a(EXLCZ)992670000000069769 100 $a20101011d2011|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical learning for biomedical data /$fJames D. Malley, Karen G. Malley, Sinisa Pajevic$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2011. 215 $a1 online resource (xii, 285 pages) $cdigital, PDF file(s) 225 1 $aPractical guides to biostatistics and epidemiology 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-69909-6 311 $a0-521-87580-3 320 $aIncludes bibliographical references and index. 327 $apt. 1. Introduction -- pt. 2. A machine toolkit -- pt. 3. Analysis fundamentals -- pt. 4. Machine strategies. 330 $aThis book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests?, neural nets, support vector machines, nearest neighbors and boosting. 410 0$aPractical guides to biostatistics and epidemiology. 606 $aMedical statistics$xData processing 606 $aBiometry$xData processing 615 0$aMedical statistics$xData processing. 615 0$aBiometry$xData processing. 676 $a614.285 700 $aMalley$b James D.$0442004 702 $aMalley$b Karen G. 702 $aPajevic$b Sinisa 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910825946103321 996 $aStatistical learning for biomedical data$94045322 997 $aUNINA