04730nam a22005655i 4500 978-0-387-84858-7 DE-He213 20260521092011.0 cr nn 008mamaa 100301s2009 xxu| s |||| 0|eng d 9780387848587 99780387848587 10.1007/978-0-387-84858-7 doi 519.5 23 Hastie, Trevor. author. The Elements of Statistical Learning [electronic resource] : Data Mining, Inference, and Prediction / by Trevor Hastie, Robert Tibshirani, Jerome Friedman. Second Edition. New York, NY : Springer New York : Imprint: Springer, 2009. XXII, 745 p. 282 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Series in Statistics, 0172-7397 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 the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. STATISTICS. DATA MINING. ARTIFICIAL INTELLIGENCE. BIOINFORMATICS. BIOLOGY DATA PROCESSING. MATHEMATICAL STATISTICS. STATISTICS. STATISTICAL THEORY AND METHODS. DATA MINING AND KNOWLEDGE DISCOVERY. COMPUTATIONAL BIOLOGY/BIOINFORMATICS. ARTIFICIAL INTELLIGENCE (INCL. ROBOTICS). COMPUTER APPL. IN LIFE SCIENCES. STATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY AND EARTH SCIENCES. Tibshirani, Robert. author. Friedman, Jerome. author. SpringerLink (Online service) Springer eBooks Printed edition: 9780387848570 Springer Series in Statistics, 0172-7397 http://dx.doi.org/10.1007/978-0-387-84858-7 Ver el texto completo en las instalaciones del CICY ZDB-2-SMA ddc ER 35166 35166 0 0 ddc 0 0 LE CICY CICY EL 2025-10-06 0 519.5 2025-10-06 08:44:23 2025-10-06 ER