TY - BOOK AU - Hastie, Trevor AU - Tibshirani, Robert AU - Friedman, Jerome TI - The Elements of Statistical Learning: Data Mining, Inference, and Prediction / SN - 9780387848570 U1 - 006.31 HAS 23rd PY - 2017/// CY - USA PB - Springier Science + Business Media Inc. KW - Supervised learning (Machine learning); Data mining; Statistics; Electronic data processing; Biology--Data processing; Computational biology; Mathematics--Data processing; Bioinformatics N1 - It includes References, Author Index and Index Pages; About the Book: 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. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. About the authors: Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. Table of contents (18 chapters) Introduction Pages 1-8 Hastie, Trevor (et al.) Preview Overview of Supervised Learning Pages 9-41 Hastie, Trevor (et al.) Preview Linear Methods for Regression Pages 43-99 Hastie, Trevor (et al.) Preview Linear Methods for Classification Pages 101-137 Hastie, Trevor (et al.) Preview Basis Expansions and Regularization Pages 139-189 Hastie, Trevor (et al.) Preview Kernel Smoothing Methods Pages 191-218 Hastie, Trevor (et al.) Preview Model Assessment and Selection Pages 219-259 Hastie, Trevor (et al.) Preview Model Inference and Averaging Pages 261-294 Hastie, Trevor (et al.) Preview Additive Models, Trees, and Related Methods Pages 295-336 Hastie, Trevor (et al.) Preview Boosting and Additive Trees Pages 337-387 Hastie, Trevor (et al.) Preview Neural Networks Pages 389-416 Hastie, Trevor (et al.) Preview Buy Chapter 25,95 € Support Vector Machines and Flexible Discriminants Pages 417-458 Hastie, Trevor (et al.) Preview Prototype Methods and Nearest-Neighbors Pages 459-483 Hastie, Trevor (et al.) Preview Unsupervised Learning Pages 485-585 Hastie, Trevor (et al.) Preview Random Forests Pages 587-604 Hastie, Trevor (et al.) Preview Ensemble Learning Pages 605-624 Hastie, Trevor (et al.) Preview Undirected Graphical Models Pages 625-648 Hastie, Trevor (et al.) Preview High-Dimensional Problems: p N Pages 649-698 Hastie, Trevor (et al.) UR - https://www.springer.com/gp/book/9780387848570 ER -