Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Paperback. Condition: 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.
Published by Cambridge University Press (edition 1), 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521637678 ISBN 13: 9780521637671
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Published by Cambridge University Press, 2011
ISBN 10: 0521875803 ISBN 13: 9780521875806
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
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Published by Cambridge University Press, 2011
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Published by Cambridge University Press CUP, 2011
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
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Published by Cambridge University Press, 2011
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ISBN 10: 0521699096 ISBN 13: 9780521699099
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, Cambridge, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Paperback. Condition: 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press 3/28/2011, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Add to basketPaperback or Softback. Condition: New. Statistical Learning for Biomedical Data 1.32. Book.
Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Add to basketTaschenbuch. Condition: 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.
Published by Cambridge University Press, Cambridge, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Add to basketPaperback. Condition: 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Cambridge University Press, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Add to basketpaperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
Published by Cambridge University Press, Cambridge, 2011
ISBN 10: 0521699096 ISBN 13: 9780521699099
Language: English
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Add to basketPaperback. Condition: 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by Cambridge University Press, 2011
ISBN 10: 0521875803 ISBN 13: 9780521875806
Language: English
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