The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics)

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome

ISBN 10: 0387952845 / ISBN 13: 9780387952840

Edité par Springer, 2003

A propos de cet article

Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if youâ re not satisfied with purchase please return item for full refund. Ships USPS Media Mail. N° de réf. du vendeur OTV.0387952845.VG

Détails bibliographiques

Titre : The Elements of Statistical Learning: Data ...

Éditeur : Springer

Date d'édition : 2003

Reliure : Couverture rigide

Etat : very_good

A propos de ce titre

Synopsis :

During the past decade, there has been an explosion in computation and information technology. With it has 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 different terminology. This book describes the important ideas in these areas in common conceptual framework. While the approach is statistical, the emphasis on concepts rather than mathematics. Many examples are given with a liberal use of color graphics. It should be 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 machines, classification trees, and boosting - the first comprehensive treatment of this topic in any book.

À propos de l?auteur:

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University and prominent researchers in this area. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in 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, and projection pursuit.