The Elements of Statistical Learning describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour graphics.
[Xem chi tiết]I. THÔNG TIN SẢN PHẨM
Mã sản phẩm : STT271
Nhà xuất bản: O'Reilly
Tác giả : Trevor Hastie, Robert Tibshirani, Jerome Friedman
Ngôn Ngữ : Tiếng Anh
ISBN : 0387848576
Số trang : 767 Pages
Hình thức : Bìa Mềm, in đen trắng
Loại : Sách gia công
Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.
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II. MÔ TẢ SẢN PHẨM
1.Mô tả sản phẩm
The Elements of Statistical Learning describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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.
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 factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
2.Tác giả
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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