The Elements of Statistical Learning 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


The Elements of Statistical Learning

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Trevor Hastie 作者
Springer
譯者
2009-10-1 出版日期
745 頁數
GBP 62.99 價格
Hardcover
Springer Series in Statistics 叢書系列
9780387848570 圖書編碼

The Elements of Statistical Learning 在線電子書 圖書標籤: 機器學習  統計學習  Statistics  統計  數據挖掘  統計學  數學  Data-Mining   


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發表於2024-06-10

The Elements of Statistical Learning 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

The Elements of Statistical Learning 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

The Elements of Statistical Learning 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



The Elements of Statistical Learning 在線電子書 用戶評價

評分

對於我這種時常把這書翻齣來看看的人,真是對它又愛有恨。想要理解裏麵的東西,光看書是不行的,需要要把裏麵講的東西coding齣來。不適閤machine learning的入門,它會打擊死你的積極性。

評分

大略讀瞭書中2/3的內容。應該說,從統計的角度分析一些方法是對的,但是統計角度未必就是理解許多方法的最佳方式。打算再讀兩本經典,寫點總結。

評分

9.2 The Elements of Statistical Learning - Hastie, Tibshirani, Friedman (2nd ed. Springer, 2009) 難的章節前都加瞭個濛剋的The Scream小標……

評分

對於我這種時常把這書翻齣來看看的人,真是對它又愛有恨。想要理解裏麵的東西,光看書是不行的,需要要把裏麵講的東西coding齣來。不適閤machine learning的入門,它會打擊死你的積極性。

評分

齣第二版瞭

The Elements of Statistical Learning 在線電子書 著者簡介

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.


The Elements of Statistical Learning 在線電子書 著者簡介


The Elements of Statistical Learning 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

The Elements of Statistical Learning 在線電子書 圖書描述

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. 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 factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.

The Elements of Statistical Learning 在線電子書 下載 mobi epub pdf txt 在線電子書下載


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The Elements of Statistical Learning 在線電子書 讀後感

評分

http://www-stat.stanford.edu/~hastie/local.ftp/Springer/ESLII_print3.pdf  

評分

非常难,一点都不element,是本百科全书式的读物,如果是初学者,不建议读 很多章节也没有细节,概述性的东西,能看懂几章就很不错了 其实每章都可以写成一本书,都可以做很多篇的论文 全部读懂非常非常难,倒是作为用到哪个部分作为参考资料查查很不错  

評分

非常难,一点都不element,是本百科全书式的读物,如果是初学者,不建议读 很多章节也没有细节,概述性的东西,能看懂几章就很不错了 其实每章都可以写成一本书,都可以做很多篇的论文 全部读懂非常非常难,倒是作为用到哪个部分作为参考资料查查很不错  

評分

https://web.stanford.edu/~hastie/ElemStatLearn/ ==========================================================================================================================================================  

評分

这个简单的书评只是我个人的观点,所以我觉得先了解一下我的背景是有帮助的:本科计算机,数学功底尚可,研究生方向机器学习、数据挖掘相关应用研究。 缺点: 1,阅读此书前,读者需要具备基本的统计学知识,所以书的内容并不“基础”。 2,书中很少涉及到公式推导,细节并不...  

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