Dynamic Fuzzy Machine Learning

Fanzhang Li; Li Zhang; Zhao Zhang

Published by De Gruyter 2018

Dynamic Fuzzy Machine Learning

Fanzhang Li , Li Zhang and Zhao Zhang

https://doi.org/10.1515/9783110520651

Overview
Contents

About this book

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 / Editor information

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

Topics

Frontmatter Publicly Available Download PDF	i
Preface Li Fanzhang Requires Authentication Unlicensed Licensed Download PDF	v
Contents Publicly Available Download PDF	ix
1. Dynamic fuzzy machine learning model Requires Authentication Unlicensed Licensed Download PDF	1
2. Dynamic fuzzy autonomic learning subspace algorithm Requires Authentication Unlicensed Licensed Download PDF	51
3. Dynamic fuzzy decision tree learning Requires Authentication Unlicensed Licensed Download PDF	69
4. Concept learning based on dynamic fuzzy sets Requires Authentication Unlicensed Licensed Download PDF	115
5. Semi-supervised multi-task learning based on dynamic fuzzy sets Requires Authentication Unlicensed Licensed Download PDF	161
6. Dynamic fuzzy hierarchical relationships Requires Authentication Unlicensed Licensed Download PDF	209
7. Multi-agent learning model based on dynamic fuzzy logic Requires Authentication Unlicensed Licensed Download PDF	267
8. Appendix Requires Authentication Unlicensed Licensed Download PDF	301
Index Requires Authentication Unlicensed Licensed Download PDF	321

(Deutsch)

Language: English
Publisher: De Gruyter
Audience: Computer science researchers and engineers.
Pages
- Front matter: 14
- Main content: 323
Illustrations
- Illustrations: 80
- Tables: 0

eBook

Published: December 4, 2017
ISBN: 9783110520651

Hardcover

Published: December 4, 2017
ISBN: 9783110518702