Self-Adaptive Systems for Machine Intelligence


Self-Adaptive Systems for Machine Intelligence
Self-Adaptive Systems for Machine Intelligence (2011) .. by Haibo He 


Table of Contents
Preface
Acknowledgments
Chapter 1. Introduction
1.1 The Machine Intelligence Research
1.2 The Two-Fold Objectives: Data-Driven and Biologically-Inspired Approaches
1.3 How to Read this Book
1.4 Summary and Further Reading
References
Chapter 2. Incremental Learning
2.1 Introduction
2.2 Problem Foundation
2.3 An Adaptive Incremental Learning Framework
2.4 Design of the Mapping Function
2.5 Case Study
2.6 Summary
Chapter 3. Imbalanced Learning
3.1 Introduction
3.2 Nature of the Imbalanced Learning
3.3 Solutions for Imbalanced Learning
3.4 Assessment Metrics for Imbalanced Learning
3.5 Opportunities and Challenges
3.6 Case Study
3.7 Summary
Chapter 4. Ensemble Learning
4.1 Introduction
4.2 Hypothesis Diversity
4.3 Developing Multiple Hypotheses
4.4 Integrating Multiple Hypotheses
4.5 Case Study
4.6 Summary
Chapter 5. Adaptive Dynamic Programming for Machine Intelligence
5.1 Introduction
5.2 Fundamental Objectives: Optimization and Prediction
5.3 ADP for Machine Intelligence
5.4 Case Study
5.5 Summary
Chapter 6. Associative Learning
6.1 Introduction
6.2 Associative Learning Mechanism
6.3 Associative Learning in Hierarchical Neural Networks
6.4 Case Study
6.5 Summary
Chapter 7. Sequence Learning
7.1 Introduction
7.2 Foundations for Sequence Learning
7.3 Sequence Learning in Hierarchical Neural Structure
7.4 Level 0: A Modified Hebbian Learning Architecture
7.5 Level 1 to Level N: Sequence Storage, Prediction and Retrieval
7.6 Memory Requirement
7.7 Learning and Anticipation of Multiple Sequences
7.8 Case Study
7.9 Summary
Chapter 8. Hardware Design for Machine Intelligence
8.1 A Final Comment
References