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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   $a9910785691903321
996   $aStatistical learning for biomedical data$93761061
997   $aUNINA