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Dynamic Fuzzy Machine Learning

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.

  • Illustrates concepts, algorithms and design for dynamic fuzzy machine learning.
  • Explains learning mechanisms based on agent ubiquitous model and Bayesian quantum stochastic model.
  • Combines theories with project experiences.

Author Information

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

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Audience: Computer science researchers and engineers.