Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series 在线电子书 图书标签: 编程 经典 統計學 機器學習 机器学习 新西蘭 數據挖掘 數學
发表于2024-11-22
Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
WEKA篇幅终于减少了
评分WEKA篇幅终于减少了
评分WEKA篇幅终于减少了
评分WEKA篇幅终于减少了
评分WEKA篇幅终于减少了
From the Back Cover
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Witten, Frank, Hall and Pal include the techniques of today as well as methods at the leading edge of contemporary research. Key Features Include: Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface. Accompanying open-access online courses that introduce practical application of the material in the book.
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About the Author
Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>Mark A. Hall holds a bachelor’s degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas.
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projectsPresents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interfaceIncludes open-access online courses that introduce practical applications of the material in the book
这本dm的书啃完了,觉得有点这个书有点“偏见”,怎么理解呢 前面的东西不错哦,可是后半部分的Weka平台我个人觉得翻翻就行了,要学还不如看看spss的书呢,前面关于机器模型的建立的数学基础要求的不是很高,所以很适合一般没有学过随机过程的人看看,要是数学很牛的人,可以看...
评分这本书虽然标题是Data Mining,但是核心内容还是机器学习。我理解“数据挖掘”主要指的还是KDD,即基于数据库的知识发现。在这个领域,基本的方法是聚类和关联规则发现;而在机器学习领域,主要研究的是分类。 这本书的内容主要是分类,也有一部分聚类的内容,关联规则发现基...
评分这本书虽然标题是Data Mining,但是核心内容还是机器学习。我理解“数据挖掘”主要指的还是KDD,即基于数据库的知识发现。在这个领域,基本的方法是聚类和关联规则发现;而在机器学习领域,主要研究的是分类。 这本书的内容主要是分类,也有一部分聚类的内容,关联规则发现基...
评分这本书确实如所知道的那样,翻译得很水。很多一些概念的东西就像把你隔在某种屏障外,然后其实说的并不是那么枯燥的东西………… 本书主要还是介绍机器学习的,用这本书的目的就是为了了解weka中算法实现的思想。从这点出发这本书还算是比较值当的了,比官方的文档确实还是要精...
评分我觉得,可以当作weka的使用手册来看,但是比weka自带的指南写的好看。 算法部分的介绍很详细。
Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024