Overview

Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.

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9783110518702

Table of Contents

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Table of Content: Chapter 1 Dynamic fuzzy machine learning1.1 Raise of dynamic fuzzy machine learning1.2 Dynamic fuzzy machine learning and model1.3 Algorithms for dynamic fuzzy machine learning systems1.4 Process control of dynamic fuzzy machine learning1.5 Algorithms for dynamic fuzzy relations1.6 SummaryChapter 2 Dynamic fuzzy autonomous learning algorithms2.1 Development of autonomous learning2.2 Theoretical framework based on DFL (Dynamic fuzzy learning) for autonomous learning sub-space2.3 Algorithms based on DFL for autonomous learning sub-space2.4 SummaryChapter 3 Dynamic fuzzy decision tree learning3.1 Development of decision tree learning3.2 Dynamic fuzzy decision tree learning3.3 Technical difficulties in dynamic fuzzy decision tree3.4 Pruning strategy in dynamic fuzzy decision treeChapter 4 Agent learning based on DFL4.1 Introduction4.2 Mental model based on DFL4.3 Single agent machine learning based on DFL4.4 Multi agent machine learning based on DFL4.5 SummaryChapter 5 Agent ubiquitous machine learning5.1 Introduction5.2 Agent ubiquitous machine learning5.3 Classifier design for agent ubiquitous machine learning5.4 SummaryChapter 6 Bayesian quantum stochastic learning6.1 Raise of Bayesian quantum stochastic learning6.2 Theoretical framework6.3 Bayesian quantum stochastic learning model6.4 Bayesian quantum stochastic learning algorithm and design for network structure6.5 Bayesian quantum stochastic learning algorithm and design for network parameter6.6 Bayesian quantum stochastic learning algorithm and design for missing data6.7 SummaryReferencesAppendix

Author Information

Fanzhang Li, Zhang Li, Zhang Zhao, Soochow University, Suzhou, China

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