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Dynamic Fuzzy Machine Learning

BuchGebunden
323 Seiten
Englisch
De Gruytererschienen am04.12.2017
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.mehr
Verfügbare Formate
BuchGebunden
EUR159,95
E-BookPDFDRM AdobeE-Book
EUR169,95
E-BookEPUBDRM AdobeE-Book
EUR169,95

Produkt

KlappentextMachine 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.
Details
ISBN/GTIN978-3-11-051870-2
ProduktartBuch
EinbandartGebunden
Erscheinungsjahr2017
Erscheinungsdatum04.12.2017
Seiten323 Seiten
SpracheEnglisch
Gewicht794 g
Illustrationen80 b/w ill., 0 b/w tbl.
Artikel-Nr.15727672

Inhalt/Kritik

Kritik
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
Appendixmehr