稀疏统计学习及其应用 统计机器学习界泰斗作品 全面介绍稀疏统计模型及其研究成果 用lasso模型解决大数据挖掘 机器学习等热点问题
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海外直订Cupboards of Curiosity 好奇的橱柜
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Product Details 基本信息 ISBN-13 书号 9780674865938 Author 作者 Hastie Format 版本 精装 Pages Number 页数 277页 Publisher 出版社 Harvard University Press Publication Date 出版日期 1983-02-05 Language 语种 其它(含多语)
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现货 英文原版 统计学习基础 数据挖掘 推理与预测 The Elements of Statistical Learni
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Thisbookisourattempttobringtogethermanyoftheimportantnewideasinlearning-andexplaintheminastatisticalframework.Whilesomemathematicaldetailsareneeded-weemphasizethemethodsandtheirconceptualunderpinningsratherthantheirtheoreticalproperties.Asaresult-wehopethatthisbookwillappealnotjusttostatisticiansbutalsotoresearchersandpractitionersinawidevarietyoffields.
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Statistical Learning with Sparsity 9781498712163
Product Details 基本信息 ISBN-13 书号 9781498712163 Author 作者 Trevor Hastie Format 版本 精装 Pages Number 页数 368页 Publisher 出版社 Chapman and Hall/CRC Publication Date 出版日期 2015-05-07 Shipping Weight 商品重量 771g Language 语种 英语 Book Contents 内容简介 In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. The authors cover the lasso for linear regression, generalized penalties, numerical methods for optimization, statistical inference methods for fitted (lasso) models, sparse multivariate analysis, graphical models, compressed sensing, and much more.
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Product Details 基本信息 ISBN-13 书号 9781857756289 Author 作者 Anne Hastie Format 版本 平装-胶订 Pages Number 页数 190页 Publisher 出版社 CRC Press Publication Date 出版日期 2005-02-01 Shipping Weight 商品重量 612g Language 语种 英语
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【预售 按需印刷】Hard Hearts and Soft Heads
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Product Details 基本信息 ISBN-13 书号 9781412959032 Author 作者 Reid Hastie and Robyn M Dawes Format 版本 平装-胶订 Pages Number 页数 392页 Publication Date 出版日期 2010-01-28 Shipping Weight 商品重量 454g Language 语种 英语 Book Contents 内容简介 The award-winning introduction now has more on the role of emotions, happiness and general well-being in decision making; research on the neuroscience of decision processes; and more on the adaptive value of non-rational heuristics.
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统计学习基础 (美)哈斯蒂(Hastie,T) 世界图书出版公司【正.版】 【正品保证,进入店铺更多优惠!】
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海外直订By-Gone Glasgow ... Forty Full-Page Drawings and Twenty-
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The Elements of Statistical Learning 9780387848570 现货图书
Product Details 基本信息 ISBN-13 书号 9780387848570 Author 作者 Trevor Hastie Stanford University Dept. of Statistics Stan Format 版本 精装 Pages Number 页数 745页 Publisher 出版社 Springer New York Publication Date 出版日期 2009-02-09 Product Dimensions 商品尺寸 16.3 x 23.6 x 3.6 cm Shipping Weight 商品重量 1.2 kg Language 语种 其它(含多语) Book Contents 内容简介 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, sup
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图书信息 书号: 9781274853127 作者: Thomas Hastie Bryce 装帧: 平装-胶订 页数: 148页 出版社: BiblioLife 尺寸: 1.0 x 0.7 x 24.6 cm 出版日期: 2012-02-01 重量: 1g 语种: 其它(含多语) 内容简介 This is a reproduction of a book published before 1923. This book may
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海外直订Statistical Learning with Sparsity 基于稀疏性的统计学习
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Statistical Learning with Sparsity
Product Details 基本信息 ISBN-13 书号 9780367738334 Author 作者 TrevorHastie Pages Number 页数 368页 Publisher 出版社 Chapman and Hall/CRC Publication Date 出版日期 20201218 Product Dimensions 商品尺寸 0 x 0 x 0 cm Shipping Weight 商品重量 680 Book Description 内容简介 Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application ofl 1 penalties to generalized linear models and support vector machines, cover generalized pen
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