Books  Data Analysis & Modelling  Data Analysis & Statistics 

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Out of Print

Series: Springer Series in Statistics

By: Trevor Hastie, Robert Tibshirani and Jerome Friedman

549 pages, 200 illus. in color.


Hardback | Dec 2001 [2003 reprint] | Edition: 3 | #174222 | ISBN: 0387952845
Out of Print Details

About this book

During the past decade there has been an explosion in computation and information technology. With it has come a vast amount of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.


Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basic Expansions and Regularization.- Kernel Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminates.- Prototype Methods and Nearest Neighbors.- Unsupervised Learning.

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