The elements of statistical learning : data mining, inference, and prediction
Trevor Hastie (Author), Robert Tibshirani (Author), J. H. Friedman (Author)
"During the past decade there has been an explosion in computation and information technology. With it have come vast amounts 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. Many examples are given, with a liberal use of color graphics."--Jacket
eBook, English, 2009
Second edition, corrected 7th printing View all formats and editions
Springer, New York, 2009
1 online resource (xxii, 745 pages) : color illustrations
9780387848587, 9781282126749, 0387848584, 1282126741
405547558
Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basis Expansions and Regularization.- Kernel Smoothing 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 Discriminants.- Prototype Methods and Nearest-Neighbors.- Unsupervised Learning.- Random Forests.- Ensemble Learning.- Undirected Graphical Models.- High-Dimensional Problems: p ? N.
Second edition corrected at 7th printing in 2013