Articoli correlati a Statistical Learning for Biomedical Data
Sinossi
This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.
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Informazioni sugli autori
James D. Malley is a Research Mathematical Statistician in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.
Karen G. Malley is president of Malley Research Programming, Inc. in Rockville, Maryland, providing statistical programming services to the pharmaceutical industry and the National Institutes of Health. She also serves on the global council of the Clinical Data Interchange Standards Consortium (CDISC) user network, and the steering committee of the Washington, DC area CDISC user network.
Sinisa Pajevic is a Staff Scientist in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
Editore:
Cambridge University Press, 2011
ISBN 10: 0521699096
ISBN 13: 9780521699099
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
Editore:
Cambridge University Press, 2011
ISBN 10: 0521699096
ISBN 13: 9780521699099
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
Editore:
Cambridge University Press, 2011
ISBN 10: 0521699096
ISBN 13: 9780521699099
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Statistical Learning for Biomedical Data
Editore:
Cambridge University Press, 2011
ISBN 10: 0521699096
ISBN 13: 9780521699099
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Paperback. Condizione: Very Good. This 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 (TM), neural nets, support vector machines, nearest neighbors and boosting. Codice articolo 0103749
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Statistical learning for biomedical data
Editore:
Cambridge University Press, 2011
ISBN 10: 0521699096
ISBN 13: 9780521699099
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
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Statistical Learning For Biomedical Data
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEJUNE24-384303
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Statistical Learning for Biomedical Data
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Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Codice articolo ABNR-36537
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STATISTICAL LEARNING FOR BIOMEDICAL DATA (PRACTICAL GUIDES TO BIOSTATISTICS AND EPIDEMIOLOGY)
Da: Basi6 International, Irving, TX, U.S.A.
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEJUNE24-105431
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Statistical Learning For Biomedical Data (Practical Guides To Biostatistics And Epidemiology)
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
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Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Codice articolo ABNR-184944
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