Pajevic Sinisa
Pajevic Sinisa - Statistical Learning For Biomedical Data - Paperback
Pajevic Sinisa - Statistical Learning For Biomedical Data - Paperback
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Binding: Paperback
Description: 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.
Title: Statistical Learning For Biomedical Data
Author(s): Pajevic Sinisa
Publisher: Cambridge University Press
Barcode: 9780521699099
Pages: 298 Pages, Worked Examples Or Exercises; 25 Tables, Black And White; 2 Plates, Unspecified; 35 Halft
Publication Date: 2/24/2011
Series: Practical Guides to Biostatistics and Epidemiology
Category: Probability & Statistics
Description: 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.
Title: Statistical Learning For Biomedical Data
Author(s): Pajevic Sinisa
Publisher: Cambridge University Press
Barcode: 9780521699099
Pages: 298 Pages, Worked Examples Or Exercises; 25 Tables, Black And White; 2 Plates, Unspecified; 35 Halft
Publication Date: 2/24/2011
Series: Practical Guides to Biostatistics and Epidemiology
Category: Probability & Statistics
