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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Spring

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Spring eBook includes PDF, ePub and Kindle version


by Trevor Hastie, Robert Tibshirani, Jerome Friedman (Hardcover)


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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Spring

Elements of Statistical Learning data mining inference ~ Data Mining Inference and Prediction Second Edition February 2009 Trevor Hastie Robert Tibshirani Jerome Friedman Whats new in the 2nd edition The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition February 2009 Trevor Hastie Robert Tibshirani

The Elements of Statistical Learning Data Mining ~ Cornell University 225 Rhodes Hall Ithaca New York 14853 6072559136 dr24 Book 9116 The Elements of Statistical Learning Data Mining Inference and Prediction TrevorHastie RobertTibshirani andJeromeFriedman NewYork Springer Verlag 2001 ISBN 0387952845 viii 533 pp 7495 H

The Elements of Statistical Learning Data Mining ~ The Elements of Statistical Learning Hastie codeveloped much of the statistical modeling software and environment in RSPLUS and invented principal curves and surfaces Tibshirani proposed the lasso and is coauthor of the very successful An Introduction to the Bootstrap Friedman is the coinventor of many datamining tools including CART MARS

The Elements of Statistical Learning Data Mining ~ inferential methods such as variational inference and crossdimensional inference nonEuclidean data such as networks images and shapes Offered in Spring Student Learning Outcomes Students will Schedule Methods Be able to perform data visualization Weeks 12 Homework Project

The Elements of Statistical Learning Data Mining ~ Request PDF on ResearchGate On Jan 1 2009 Trevor Hastie and others published The Elements of Statistical Learning Data Mining Inference and Prediction

The Elements of Statistical Learning Data Mining ~ While the approach is statistical the emphasis is on concepts rather than mathematics Many examples are given with a liberal use of colour graphics It is a valuable resource for statisticians and anyone interested in data mining in science or industry The books coverage is broad from supervised learning prediction to unsupervised learning

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