THE ELEMENTS OF STATISTICAL LEARNING: DATA MINING, INFERENCE, AND PREDICTION

Hastie, Trevor & Tibshirani, Robert J. & Friedman, Jerome

ISBN 10: 0387952845 / ISBN 13: 9780387952840

Edité par Springer, 2002

A propos de cet article

2002. Very good condition with no wrapper. Springer Series in Statistics. Data mininbg, machine learning, and bioinformatics. 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. Matt yellow and brown boards. xvi and 533 pages including index. 2nd printing. Boards lightly scuffed and marked. Text block a little grubby. Name in ink to top edge of front endpaper. Contents clean. Packaged with care and promptly dispatched!

N° de réf. du vendeur 1830310

Détails bibliographiques

• Titre: THE ELEMENTS OF STATISTICAL LEARNING: DATA MINING, INFERENCE, AND PREDICTION
• Éditeur: Springer
• Date de parution: 2002
• ISBN à 10 chiffres: 0387952845
• ISBN à 13 chiffres: 9780387952840
• Reliure: Hardback
• État de l'article: Very Good
• État de la jaquette: No Jacket

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.