TY  - BOOK
AU  - Hastie,Trevor
AU  - Tibshirani,Robert
AU  - Friedman,J.H.
TI  - The elements of statistical learning: data mining, inference, and prediction 
T2  - Springer series in statistics,
SN  - 9780387848570
AV  - Q325.75 .H37 2009
U1  - 006.3122 HAS 2009 23/eng/20221220
PY  - 2009///]
CY  - New York
PB  - Springer
KW  - Supervised learning (Machine learning)
KW  - Electronic data processing
KW  - Statistics
KW  - Biology
KW  - Data processing
KW  - Computational biology
KW  - Mathematics
KW  - Data mining
KW  - Artificial intelligence
KW  - Learning
KW  - Artificial Intelligence
KW  - Algorithms
KW  - Computing Methodologies
KW  - Statistics as Topic
KW  - Computational Biology
KW  - Data Mining
KW  - Statistik
KW  - gnd
KW  - lcgft
N1  - Includes bibliographical references (pages 699-727) and indexes; 1; Introduction --; 2; Overview of supervised learning --; 3; Linear methods for regression --; 4; Linear methods for classification --; 5; Basis expansions and regularization --; 6; Kernel smoothing methods --; 7; Model assessment and selection --; 8; Model inference and averaging --; 9; Additive models, trees, and related methods --; 10; Boosting and additive trees --; 11; Neural networks --; 12; Support vector machines and flexible discriminants --; 13; Prototype methods and nearest-neighbors --; 14; Unsupervised learning --; 15; Random forests --; 16; Ensemble learning --; 17; Undirected graphical models --; 18; High-dimensional problems: p>> N
N2  - "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."--Publisher's description
ER  -