《数据挖掘-实用机器学习工具与技术-(英文版.第4版)》(新西兰)伊恩H.威腾(IanH.W | PDF下载|ePub下载
数据挖掘-实用机器学习工具与技术-(英文版.第4版) 版权信息
- 出版社：机械工业出版社
- 出版时间：2017-04-01
- ISBN：9787111565277
- 条形码：9787111565277 ; 978-7-111-56527-7
数据挖掘-实用机器学习工具与技术-(英文版.第4版) 本书特色
本书是数据挖掘和机器学习领域的经典畅销教材，被国内外众多名校选用。第4版全面反映了该领域的新技术变革，包括关于概率方法和深度学习的重要新章节。此外，备受欢迎的机器学习软件Weka再度升级，读者可以在友好的交互界面中执行数据挖掘任务。书中的基础知识清晰详细，实践工具和技术指导具体实用，不仅适合作为高等院校相关专业的本科生或研究生教材，也可供广大技术人员参考。
数据挖掘-实用机器学习工具与技术-(英文版.第4版) 内容简介
本书是数据挖掘和机器学习领域的经典畅销教材，被国内外众多名校选用。第4版全面反映了该领域的新技术变革，包括关于概率方法和深度学习的重要新章节。此外，备受欢迎的机器学习软件Weka再度升级，读者可以在友好的交互界面中执行数据挖掘任务。书中的基础知识清晰详细，实践工具和技术指导具体实用，不仅适合作为高等院校相关专业的本科生或研究生教材，也可供广大技术人员参考。
数据挖掘-实用机器学习工具与技术-(英文版.第4版) 目录
Preface
PART I INTRODUCTION TO DATA MINING
CHAPTER 1 What’s it all about?
1.1 Data Mining and Machine Learning
Describing Structural Patterns
Machine Learning
Data Mining
1.2 Simple Examples: The Weather Problem and Others
The Weather Problem
Contact Lenses: An Idealized Problem
Irises: A Classic Numeric Dataset
CPU Performance: Introducing Numeric Prediction
Labor Negotiations: A More Realistic Example
Soybean Classification: A Classic Machine Learning Success
1.3 Fielded Applications
Web Mining
Decisions Involving Judgment
Screening Images
Load Forecasting
Diagnosis
Marketing and Sales
Other Applications
1.4The Data Mining Process
1.5 Machine Learning and Statistics
1.6 Generalization as Search
Enumerating the Concept Space
Bias
1.7 Data Mining and Ethics
Reidentification
Using Personal Information
Wider Issues
1.8 Further Reading and Bibliographic Notes
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
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
List of Figures
List of Tables
PART I INTRODUCTION TO DATA MINING
CHAPTER 1 What’s it all about?
1.1 Data Mining and Machine Learning
Describing Structural Patterns
Machine Learning
Data Mining
1.2 Simple Examples: The Weather Problem and Others
The Weather Problem
Contact Lenses: An Idealized Problem
Irises: A Classic Numeric Dataset
CPU Performance: Introducing Numeric Prediction
Labor Negotiations: A More Realistic Example
Soybean Classification: A Classic Machine Learning Success
1.3 Fielded Applications
Web Mining
Decisions Involving Judgment
Screening Images
Load Forecasting
Diagnosis
Marketing and Sales
Other Applications
1.4The Data Mining Process
1.5 Machine Learning and Statistics
1.6 Generalization as Search
Enumerating the Concept Space
Bias
1.7 Data Mining and Ethics
Reidentification
Using Personal Information
Wider Issues
1.8 Further Reading and Bibliographic Notes
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
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
List of Figures
List of Tables
