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

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