The elements of statistical learning : data mining, inference, and prediction

By: Hastie, TrevorContributor(s): Tibshirani, Robert | Friedman, J. HMaterial type: TextTextLanguage: English Series: Springer series in statisticsPublication details: New York, NY : Springer, 2009Edition: 2nd edDescription: xxii, 745 p. : ill. (some col.) ; 25 cmISBN: 9780387848570Subject(s): Machine learning | Statistics | Data mining | Bioinformatics | Computational intelligence | Khai thác dữ liệu | Số liệu thống kêDDC classification: 006.3 Online resources: Click here to access online Summary: 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...
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006.3 E200L Available 2019-10-21 NV.0008020
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006.3 Available ebook TVS.000310
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Includes bibliographical references (p. [699]-727) and indexes.

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...

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