Data Mining Practical Machine Learning Tools and Techniques 4th Edition

Data Mining Practical Machine Learning Tools and Techniques Fourth Edition(Morgan Kaufmann Series in Data Management Systems)
by: Ian H. Witten – Eibe Frank – Mark A. Hall – Christopher J. Pal
ISBN-10: 0128042915
ISBN-13: 9780128042915
Edition 版本:‏ 4
Released: 2016-12-01
Pages: 654
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.
Please visit the book companion website at  http://www.cs.waikato.ac.nz/ml/weka/book.html
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource,with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents,highlighting the many new sections in the 4th edition,along with reviews of the 1st edition,errata,etc.
Provides a thorough grounding in machine learning concepts,as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit,a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
作者简介
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.
目录
Part I: Introduction to data mining
Chapter 1. What’s it all about?
Chapter 2. Input: Concepts,instances,attributes
Chapter 3. Output: Knowledge representation
Chapter 4. Algorithms: The basic methods
Chapter 5. Credibility: Evaluating what’s been learned
Part II: More advanced machine learning schemes
Part II. More advanced machine learning schemes
Chapter 6. Trees and rules
Chapter 7. Extending instance-based and linear models
Chapter 8. Data transformations
Chapter 9. Probabilistic methods
Chapter 10. Deep learning
Chapter 11. Beyond supervised and unsupervised learning
Chapter 12. Ensemble learning
Chapter 13. Moving on: applications and beyond
数据挖掘: 实用机器学习工具和技术，第四版，提供了机器学习概念的彻底基础，以及在现实世界数据挖掘情况下应用这些工具和技术的实用建议。这个备受期待的第四版最受欢迎的数据挖掘和机器学习工作向读者介绍了他们需要知道的一切，从准备投入，解释产出，评估结果到成功的数据挖掘方法的核心算法方法。
广泛的更新反映了自上一版以来在该领域发生的技术变化和现代化，包括关于概率方法和深度学习的大量新章节。随附这本书是怀卡托大学流行的WEKA机器学习软件的新版本。作者Witten，Frank，Hall和Pal包括今天的技术以及当代研究前沿的方法。
请访问书籍伴侣网站http://www.cs.waikato.ac.nz/ml/weka/book.html
它包含
Powerpoint幻灯片为第1-12章。这是一个非常全面的教学资源，许多PPT幻灯片涵盖了本书的每一章
Weka工作台上的在线附录;这是一本非常全面的学习帮助的开源软件，随着这本书
目录，强调第四版的许多新章节，以及第一版，勘误表等的评论。
提供机器学习概念的彻底基础，以及将数据挖掘项目应用于工具和技术的实用建议
介绍通过在机器学习方法中转换输入或输出来实现性能改进的具体提示和技术
包括一个可下载的WEKA软件工具包，用于数据挖掘任务的全面收集机器学习算法 – 在易于使用的交互界面
包括开放式在线课程，介绍本书中材料的实际应用
作者简介
Ian H. Witten是新西兰怀卡托大学计算机科学教授。他指导新西兰数字图书馆研究项目。他的研究兴趣包括信息检索，机器学习，文本压缩和演示编程。他在英国剑桥大学获得数学硕士学位;加拿大卡尔加里大学计算机科学硕士学位;以及英国埃塞克斯大学的电气工程博士学位。他是ACM和新西兰皇家学会会员。他广泛出版了数字图书馆，机器学习，文本压缩，超文本，语音合成和信号处理以及计算机排版。他写了几本书，最新的是管理千兆字节（1999）和数据挖掘（2000），两者都来自摩根考夫曼。
Eibe Frank与他的萨摩亚配偶和两个可爱的男孩住在新西兰，但最初来自德国，他在卡尔斯鲁厄大学获得了计算机科学学士学位。他搬到新西兰去追求博士学位。在Ian H. Witten的监督下进行机器学习，并加入了怀卡托大学计算机科学系，担任完成学业的讲师。他现在是同一机构的副教授。作为Java编程语言的早期采用者，他为本书中描述的Weka软件奠定了基础。他为文学贡献了一些关于机器学习和数据挖掘的出版物，并在这些领域参考了许多会议和期刊。
马克·霍尔拥有计算和数学科学学士学位和博士学位。在计算机科学方面，都来自怀卡托大学。作为一名开源商业智能软件公司Pentaho的软件开发商和数据挖掘顾问，他在怀卡托的任职期间，作为计算机科学学生和讲师，最近一直是本文所述的Weka软件的核心贡献者。书。他出版了一些关于机器学习和数据挖掘的文章，并在这些领域参加了会议和期刊。
目录
第一部分: 数据挖掘简介
第一章是什么？
输入: 概念，实例，属性
输出: 知识表示
算法: 基本方法
可信度: 评估所学到的内容
第二部分: 更先进的机器学习方案
第二部分更先进的机器学习方案
树木和规则
第7章扩展实例和线性模型
第八章数据转换
概率方法
第十章深度学习
第11章超越监督和无监督学习
第12章合奏学习
第13章继续: 应用程序和其他