Self-Adaptive Systems for Machine Intelligence
Self-Adaptive Systems for Machine Intelligence
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发表时间：
2011-08
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通讯作者：
Haibo He
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作者：
Haibo He
前言。致谢。第1章。引言。1.1机器智能研究。1.2双重目标：数据驱动和生物学启发的方法。1.3如何阅读本书。1.4总结和深入阅读。参考文献。第二章。增量学习。2.1简介。2.2问题基础。2.3自适应增量学习框架。2.4映射函数的设计。2.5案例研究。2.6总结。第三章。学习不平衡3.1简介3.2学习不平衡的本质3.3学习不平衡的解决方案3.4学习不平衡的评估指标3.5机遇与挑战3.6案例分析3.7总结第四章。集成学习。4.1介绍。4.2假设多样性。4.3发展多个假设。4.4整合多个假设。4.5案例研究。4.6总结。第五章。机器智能的自适应动态规划。5.1简介。5.2基本目标：优化与预测。5.3机器智能的ADP。5.4案例研究。5.5总结。第六章。关联学习。6.1介绍。6.2关联学习机制。6.3层次神经网络中的关联学习。6.4案例研究。6.5总结。第七章。序列学习。7.1简介。7.2序列学习的基础。7.3层次神经结构中的序列学习。7.4 0级：一种改进的Hebbian学习架构。7.5 1级到N级：序列存储、预测和检索。7.6内存需求。7.7多序列的学习和预测。7.8案例研究。7.9总结。第八章。机器智能的硬件设计。8.1最后的评论。参考文献。
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.