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Statistical learning from a regression perspective

By: Berk, Richard A.
Material type: materialTypeLabelBookSeries: Springer series in statistics. Publisher: New York Springer Science+Business Media 2008Description: xvii, 358 p. : ill. ; 25 cm.ISBN: 9780387775005.Subject(s): Regression analysisDDC classification: 519.536 Summary: Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. (http://www.springer.com/statistics/social+sciences+%26+law/book/978-0-387-77500-5)
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Slot 1426 (0 Floor, East Wing) 519.536 B3S8 (Browse shelf) Available 173705

Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. (http://www.springer.com/statistics/social+sciences+%26+law/book/978-0-387-77500-5)

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