| 书目名称 | The Elements of Statistical Learning | | 副标题 | Data Mining, Inferen | | 编辑 | Trevor Hastie,Robert Tibshirani,Jerome Friedman | | 视频video | | | 概述 | The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book.Includes more than 200 pages of four-c | | 丛书名称 | Springer Series in Statistics | | 图书封面 |  | | 描述 | .This book 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.. | | 出版日期 | Textbook 2009Latest edition | | 关键词 | Averaging; Boosting; Projection pursuit; Random Forest; Support Vector Machine; classification; clustering | | 版次 | 2 | | doi | https://doi.org/10.1007/978-0-387-84858-7 | | isbn_ebook | 978-0-387-84858-7Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer Science+Business Media, LLC, part of Springer Nature 2009 |
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发表于 2025-3-21 17:22:17
