05239nam a22007335i 4500 000717961 DE-He213 20240923053748.0 20240312114311.0 cr nn 008mamaa 100301s2009 xxu| s |||| 0|eng d NEZPRACOVANÝ IMPORT 9780387848587 978-0-387-84858-7 10.1007/978-0-387-84858-7 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema 006.3 23 Hastie, Trevor. author. aut http://id.loc.gov/vocabulary/relators/aut The Elements of Statistical Learning [elektronický zdroj] : Data Mining, Inference, and Prediction, Second Edition / by Trevor Hastie, Robert Tibshirani, Jerome Friedman. 2nd ed. 2009. New York, NY : Springer New York : Imprint: Springer, 2009. XXII, 745 p. 658 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Series in Statistics, 2197-568X 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. 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. 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 theBootstrap. Friedman is the co-inventor of elektronické knihy fd186907 czenas electronic books eczenas Artificial intelligence. Data mining. Probabilities. Statistics . Bioinformatics. Artificial Intelligence. Data Mining and Knowledge Discovery. Probability Theory. Statistical Theory and Methods. Computational and Systems Biology. Tibshirani, Robert. author. aut http://id.loc.gov/vocabulary/relators/aut Friedman, Jerome. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) Springer Nature eBook Printed edition: 9780387848570 Printed edition: 9780387848846 Printed edition: 9781071621226 Springer Series in Statistics, 2197-568X https://zdroje.vse.cz/login?url=https://doi.org/10.1007/978-0-387-84858-7 Plný text SPRINGER-OWNED EB PhDinfra PAR 000222437 UEP01 776 E-book Tištěná verze NEPOSILAT EB ZDB-2-SMA ZDB-2-SXMS Mathematics and Statistics (SpringerNature-11649) Mathematics and Statistics (R0) (SpringerNature-43713)