DDC 519.5
Tác giả CN Hastie, Trevor.
Nhan đề The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, J H Friedman.
Lần xuất bản Second edition.
Thông tin xuất bản New York : Springer, 2009.
Mô tả vật lý xxii, 739 p. : ill. (some color), charts ; 29 cm.
Tóm tắt 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.
Từ khóa tự do Statistics.
Từ khóa tự do Electronic data processing.
Từ khóa tự do Supervised learning (Machine learning).
Khoa Công nghệ thông tin.
Chuyên ngành Truyền thông đa phương tiện.
Môn học Trí tuệ nhân tạo
Môn học Khai phá dữ liệu lớn
Môn học Học máy và ứng dụng.
Tác giả(bs) CN Friedman, Jerome.
Tác giả(bs) CN Tibshirani, Robert.
Địa chỉ 100TK_Tiếng Anh(1): 000109764
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0410 |aeng
044 |anyu
08204|a519.5|bHAS
1001|aHastie, Trevor.
24514|aThe elements of statistical learning : |bdata mining, inference, and prediction / |cTrevor Hastie, Robert Tibshirani, J H Friedman.
250 |aSecond edition.
260 |aNew York : |bSpringer,|c2009.
300 |axxii, 739 p. :|bill. (some color), charts ;|c29 cm.
520 |aDuring 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.
6530 |aStatistics.
6530 |aElectronic data processing.
6530 |aSupervised learning (Machine learning).
690|aCông nghệ thông tin.
691|aTruyền thông đa phương tiện.
692|aTrí tuệ nhân tạo
692|aKhai phá dữ liệu lớn
692|aHọc máy và ứng dụng.
7001 |aFriedman, Jerome.
7001 |aTibshirani, Robert.
852|a100|bTK_Tiếng Anh|j(1): 000109764
890|a1|b0|c0|d0
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