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Li, Fanzhang / Zhang, Li / Zhang, Zhao

Dynamic Fuzzy Machine Learning

    129,95 € / $149.99 / £106.99*

    eBook (EPUB)
    To be published:
    December 2017
    ISBN
    978-3-11-051875-7
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    Overview

    • 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.

    Aims and Scope

    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 Bayesian quantum stochastic learning for various environments. Demonstrating theories with practical examples, the book is of interests to computer scientists and engineers on artificial intelligence.

    Details

    Approx. x, 430 pages
    Language:
    English
    Type of Publication:
    Monograph

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

    Reviews

    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

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