1st Edition

Learning with Uncertainty

By Xizhao Wang, Junhai Zhai Copyright 2017
    239 Pages
    by CRC Press

    240 Pages 75 B/W Illustrations
    by CRC Press

    Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc.

    Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.


    Symbols and Abbreviations

    Chapter 1 Uncertainty 13

    Chapter 2 Decision Tree with Uncertainty

    Chapter 3 Clustering under Uncertainty Environment 77

    Chapter 4 Active Learning with Uncertainty 121

    Chapter 5 Ensemble learning with Uncertainty 173



    Xizhao Wang, Junhai Zhai