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Self-Adaptive Systems for Machine Intelligence / Edition 1

Self-Adaptive Systems for Machine Intelligence / Edition 1

by Haibo He
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Product Details

ISBN-13: 9780470343968
Publisher: Wiley
Publication date: 08/09/2011
Pages: 248
Product dimensions: 6.10(w) x 9.30(h) x 0.70(d)

About the Author

Haibo He, PhD, is Assistant Professor in the Department ofElectrical, Computer, and Biomedical Engineering at the Universityof Rhode Island. His primary research interest is computationalintelligence and self-adaptive systems, including optimization andprediction, biologically inspired machine intelligence, machinelearning and data mining, hardware design (VLSI/FPGA) for machineintelligence, as well as various application fields such as smartgrid, sensor networks, and cognitive radio networks.

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Table of Contents



Chapter 1. Introduction.

1.1 The Machine Intelligence Research.

1.2 The Two-Fold Objectives: Data-Driven andBiologically-Inspired Approaches.

1.3 How to Read this Book.

1.4 Summary and Further Reading.


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 MachineIntelligence.

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 andRetrieval.

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.


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From the Publisher

"This comprehensive introduction to machine intelligence engineering and self-adaptive systems provides an overview of a variety of processes and technologies for the development of artificial intelligence." (Book News, 1 October 2011)

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