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Detalles bibliogr�ficos

T�tulo: The Elements of Statistical Learning : Data Mining, Inference, and Prediction
Editor: Springer
Fecha de publicaci�n: 2003
Idioma: Ingl�s
ISBN 10: 0387952845
ISBN 13: 9780387952840
Encuadernaci�n: Encuadernaci�n de tapa dura
Estado: Very Good
Peso: 2,425 libras

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Sinopsis

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.

Rese�a del editor

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 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 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 & 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.

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Better World Books is a for-profit, socially conscious business and a global online bookseller that collects and sells new and used books online, matching each purchase with a book donation. Each sale generates funds for literacy and education initiatives in the U.S., the UK, and around the world. Since its launch in 2003, Better World Books has raised over $35 million for libraries and literacy, donated over 38 million books, and reused or recycled more than 475 million books.

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