The Elements of Statistical Learning 2024 pdf epub mobi 电子书

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The Elements of Statistical Learning 2024 pdf epub mobi 电子书著者简介

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.

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发表于2024-05-05

The Elements of Statistical Learning 2024 pdf epub mobi 电子书

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有人给我推荐这本书的时候说，有了这本书，就不再需要其他的机器学习教材了。入手这本书的接下来两个月，我与教材中艰深的统计推断、矩阵、数值算法、凸优化等数学知识展开艰苦的斗争。于是我明白了何谓”不需要其他的机器学习教材“：准确地说，是其他的教材都不需要了；一本...

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统计学习的经典教材，数学难度适中，英文难度较低，看了其中有监督学习部分，无监督学习部分没怎么看，算法比较经典，但是也比较老。

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

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中文翻译版大概是用google翻译翻的，然后排版一下，就出版了。所以中文翻译版中，每个单词翻译是对的，但一句话连起来却怎么也看不懂。最佳阅读方式是，看英文版，个别单词不认识的话，再看中文版对应的那个词。但如果英文版整个句子都不懂的话，那只有去借助baidu/google，并...

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出版者:Springer

作者:T. Hastie

出品人:

页数:520

译者:

出版时间:2003-07-30

价格:USD 89.95

装帧:Hardcover

isbn号码:9780387952840

丛书系列:

图书标签: 机器学习统计学习数据挖掘统计学 Statistics 数学 Learning Data-Mining

The Elements of Statistical Learning 2024 pdf epub mobi 电子书图书描述

During the past decade there has been an explosion in computation and information technology. With it has 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 descibes 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 should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (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. 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 wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful <EM>An Introduction to the Bootstrap</EM>. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

The Elements of Statistical Learning 2024 pdf epub mobi 电子书

想要找书就要到本本书屋

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