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Sinopsis
This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.
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Acerca de los autores
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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- EditorialCambridge University Press
- Año de publicación2011
- ISBN 10 0521875803
- ISBN 13 9780521875806
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas298
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
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Cambridge University Press, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
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Cambridge University Press, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Statistical Learning for Biomedical Data
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Statistical Learning for Biomedical Data (Hardcover)
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Cambridge University Press, Cambridge, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Hardcover. Condición: new. Hardcover. 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, neural nets, support vector machines, nearest neighbors and boosting. Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9780521875806
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Statistical Learning for Biomedical Data
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ISBN 10: 0521875803
ISBN 13: 9780521875806
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontech. Nº de ref. del artículo: 446951775
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Statistical Learning for Biomedical Data (Practical Guides to Biostatistics and Epidemiology)
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Cambridge University Press, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Statistical Learning for Biomedical Data
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Statistical Learning for Biomedical Data
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Cambridge University Press CUP, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Condición: New. pp. 298 Index. Nº de ref. del artículo: 262063842
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Statistical Learning for Biomedical Data (Hardcover)
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Cambridge University Press, Cambridge, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Hardcover. Condición: new. Hardcover. 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, neural nets, support vector machines, nearest neighbors and boosting. Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9780521875806
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Statistical Learning for Biomedical Data
Publicado por
Cambridge University Press, 2011
ISBN 10: 0521875803
ISBN 13: 9780521875806
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Librería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 298 47 Illus. Nº de ref. del artículo: 6865469
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