Statistical learning biomedical data von malley james (33 Ergebnisse)

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Zustand: New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 247 x 175 x 19. Weight in Grams: 596. . 2011. Illustrated. paperback. . . . . …

Zustand: New. In English.

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Zustand: New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 247 x 175 x 19. Weight in Grams: 596. . 2011. Illustrated. paperback. . . . . Books ship from the US and Ireland. …

Paperback. Zustand: Brand New. 1st edition. 312 pages. 9.61x6.85x0.87 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Zustand: New.

Zustand: New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 253 x 174 x 24. Weight in Grams: 754. . 2011. 1st Edition. hardcover. . . . . …

Zustand: New. In English.

Zustand: New. This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures. Series: Practical Guides to Biostatistics and Epidemiology. Num Pages: 298 pages, 47 b/w illus. 25 tables. BIC Classification: MBNS. Category: (U) Tertiary Education (US: College). Dimension: 253 x 174 x 24. Weight in Grams: 754. . 2011. 1st Edition. hardcover. . . . . Books ship from the US and Ireland. …

Zustand: New. pp. 298 Index.

Hardcover. Zustand: Brand New. 1st edition. 312 pages. 9.84x7.01x0.94 inches. In Stock.

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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Kartoniert / Broschiert. Zustand: 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. …

Taschenbuch. Zustand: Neu. Statistical Learning for Biomedical Data | James D. Malley (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2011 | Cambridge University Press | EAN 9780521699099 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Hardcover. Zustand: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…