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实用机器学习

作者：Sunila Gollapudi

出版社：机械工业出版社

ISBN：9787111598886

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Data Mining - Practical Machine Learning Tools and Techniques-3rd(数据挖掘-实用机器学习技术) 评分:

Part I: Practical Machine Learning Tools and Techniques 1. What’s it all about? 1.1 Data Mining and Machine Learning 1.2 Simple Examples: The Weather Problem and Others 1.3 Fielded Applications 1.4 Machine Learning and Statistics 1.5 Generalization as Search 1.6 Data Mining and Ethics 1.7 Further Reading 2. Input: Concepts, instances, attributes 2.1 What’s a Concept? 2.2 What’s in an Example? 2.3 What’s in an Attribute? 2.4 Preparing the Input 2.5 Further Reading 3. Output: Knowledge representation 3.1 Tables 3.2 Linear Models 3.3 Trees 3.4 Rules 3.5 Instance-Based Representation 3.6 Clusters 3.7 Further Reading 4. Algorithms: The basic methods 4.1 Inferring Rudimentary Rules 4.2 Statistical Modeling 4.3 Divide-and-Conquer: Constructing Decision Trees 4.4 Covering Algorithms: Constructing Rules 4.5 Mining Association Rules 4.6 Linear Models 4.7 Instance-Based Learning 4.8 Clustering 4.9 Multi-Instance Learning 4.10 Further Reading 4.11 Weka Implementations 5. Credibility: Evaluating what’s been learned 5.1 Training and Testing 5.2 Predicting Performance 5.3 Cross-Validation 5.4 Other Estimates 5.5 Comparing Data Mining Schemes 5.6 Predicting Probabilities 5.7 Counting the Cost 5.8 Evaluating Numeric Prediction 5.9 The Minimum Description Length Principle 5.10 Applying MDL to Clustering 5.11 Further Reading Part II: Advanced Data Mining 6. Implementations: Real machine learning schemes 6.1 Decision Trees 6.2 Classification Rules 6.3 Association Rules 6.4 Extending Linear Models 6.5 Instance-Based Learning 6.6 Numeric Prediction with Local Linear Models 6.7 Bayesian Networks 6.8 Clustering 6.9 Semisupervised Learning 6.10 Multi-Instance Learning 6.11 Weka Implementations 7. Data Transformations 7.1 Attribute Selection 7.2 Discretizing Numeric Attributes 7.3 Projections 7.4 Sampling 7.5 Cleansing 7.6 Transforming Multiple Classes to Binary Ones 7.7 Calibrating Class Probabilities 7.8 Further Reading 7.9 Weka Implementations 8. Ensemble Learning 8.1 Combining Multiple Models 8.2 Bagging 8.3 Randomization 8.4 Boosting 8.5 Additive Regression 8.6 Interpretable Ensembles 8.7 Stacking 8.8 Further Reading 8.9 Weka Implementations 9. Moving on: Applications and Beyond 9.1 Applying Data Mining 9.2 Learning from Massive Datasets 9.3 Data Stream Learning 9.4 Incorporating Domain Knowledge 9.5 Text Mining 9.6 Web Mining 9.7 Adversarial Situations 9.8 Ubiquitous Data Mining 9.9 Further Reading Part III: The Weka Data Mining Workbench 10. Introduction to Weka 10.1 What’s in Weka? 10.2 How Do You Use It? 10.3 What Else Can You Do? 11. The Explorer 11.1 Getting Started 11.2 Exploring the Explorer 11.3 Filtering Algorithms 11.4 Learning Algorithms 11.5 Meta-Learning Algorithms 11.6 Clustering Algorithms 11.7 Association-Rule Learners 11.8 Attribute Selection 12. The Knowledge Flow Interface 12.1 Getting Started 12.2 Knowledge Flow Components 12.3 Configuring and Connecting the Components 12.4 Incremental Learning 13. The Experimenter 13.1 Getting Started 13.2 Simple Setup 13.3 Advanced Setup 13.4 The Analyze Panel 13.5 Distributing Processing over Several Machines 14. The Command-Line Interface 14.1 Getting Started 14.2 The Structure of Weka 14.3 Command-Line Options 15. Embedded Machine Learning 15.1 A Simple Data Mining Application 16. Writing New Learning Schemes 16.1 An Example Classifier 16.2 Conventions for Implementing Classifiers 17. Tutorial Excercises for the Weka Explorer 17.1 Introduction to the Explorer Interface 17.2 Nearest-Neighbor Learning and Decision Trees 17.3 Classification Boundaries 17.4 Preprocessing and Parameter Tuning 17.5 Document Classification 17.6 Mining Association Rules References Index

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