Part II: More advanced machine learning schemes
by Christopher J. Pal, Mark A. Hall, Eibe Frank, Ian H. Witten
Data Mining, 4th Edition
- Cover image
- Title page
- Table of Contents
- Copyright
- List of Figures
- List of Tables
- Preface
- 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
- Abstracts
- 4.1 Inferring Rudimentary Rules
- 4.2 Simple Probabilistic 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 and Bibliographic Notes
- 4.11 Weka Implementations
- Chapter 5. Credibility: Evaluating what’s been learned
- Abstract
- 5.1 Training and Testing
- 5.2 Predicting Performance
- 5.3 Cross-Validation
- 5.4 Other Estimates
- 5.5 Hyperparameter Selection
- 5.6 Comparing Data Mining Schemes
- 5.7 Predicting Probabilities
- 5.8 Counting the Cost
- 5.9 Evaluating Numeric Prediction
- 5.10 The MDL Principle
- 5.11 Applying the MDL Principle to Clustering
- 5.12 Using a Validation Set for Model Selection
- 5.13 Further Reading and Bibliographic Notes
- 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
- Abstract
- 9.1 Foundations
- 9.2 Bayesian Networks
- 9.3 Clustering and Probability Density Estimation
- 9.4 Hidden Variable Models
- 9.5 Bayesian Estimation and Prediction
- 9.6 Graphical Models and Factor Graphs
- 9.7 Conditional Probability Models
- 9.8 Sequential and Temporal Models
- 9.9 Further Reading and Bibliographic Notes
- 9.10 Weka Implementations
- Chapter 10. Deep learning
- Abstract
- 10.1 Deep Feedforward Networks
- 10.2 Training and Evaluating Deep Networks
- 10.3 Convolutional Neural Networks
- 10.4 Autoencoders
- 10.5 Stochastic Deep Networks
- 10.6 Recurrent Neural Networks
- 10.7 Further Reading and Bibliographic Notes
- 10.8 Deep Learning Software and Network Implementations
- 10.9 WEKA Implementations
- Chapter 11. Beyond supervised and unsupervised learning
- Chapter 12. Ensemble learning
- Chapter 13. Moving on: applications and beyond
- Abstract
- 13.1 Applying Machine Learning
- 13.2 Learning From Massive Datasets
- 13.3 Data Stream Learning
- 13.4 Incorporating Domain Knowledge
- 13.5 Text Mining
- 13.6 Web Mining
- 13.7 Images and Speech
- 13.8 Adversarial Situations
- 13.9 Ubiquitous Data Mining
- 13.10 Further Reading and Bibliographic Notes
- 13.11 WEKA Implementations
- Appendix A. Theoretical foundations
- Appendix B. The WEKA workbench
- References
- Index
-
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