Description: This book develops a self-contained framework for machine learning based on dynamic fuzzy model via elabrating on concepts and algorithms. It further explains mechanisms for agent learning and agent ubiquitous learning, and discusses design of Bayes
Brief description: Fanzhang Li, Zhang Li, Zhang Zhao, Soochow University, Suzhou, China
Review Quotes: Table of Content:
Chapter 1 Dynamic fuzzy machine learning
1.1 Raise of dynamic fuzzy machine learning
1.2 Dynamic fuzzy machine learning and model
1.3 Algorithms for dynamic fuzzy machine learning systems
1.4 Process control of dynamic fuzzy machine learning
1.5 Algorithms for dynamic fuzzy relations
1.6 Summary
Chapter 2 Dynamic fuzzy autonomous learning algorithms
2.1 Development of autonomous learning
2.2 Theoretical framework based on DFL (Dynamic fuzzy learning) for autonomous learning sub-space
2.3 Algorithms based on DFL for autonomous learning sub-space
2.4 Summary
Chapter 3 Dynamic fuzzy decision tree learning
3.1 Development of decision tree learning
3.2 Dynamic fuzzy decision tree learning
3.3 Technical difficulties in dynamic fuzzy decision tree
3.4 Pruning strategy in dynamic fuzzy decision tree
Chapter 4 Agent learning based on DFL
4.1 Introduction
4.2 Mental model based on DFL
4.3 Single agent machine learning based on DFL
4.4 Multi agent machine learning based on DFL
4.5 Summary
Chapter 5 Agent ubiquitous machine learning
5.1 Introduction
5.2 Agent ubiquitous machine learning
5.3 Classifier design for agent ubiquitous machine learning
5.4 Summary
Chapter 6 Bayesian quantum stochastic learning
6.1 Raise of Bayesian quantum stochastic learning
6.2 Theoretical framework
6.3 Bayesian quantum stochastic learning model
6.4 Bayesian quantum stochastic learning algorithm and design for network structure
6.5 Bayesian quantum stochastic learning algorithm and design for network parameter
6.6 Bayesian quantum stochastic learning algorithm and design for missing data
6.7 Summary
References
Appendix