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.
By:
Xizhao Wang, Junhai Zhai Imprint: CRC Press Country of Publication: United Kingdom Dimensions:
Height: 234mm,
Width: 156mm,
Weight: 453g ISBN:9780367574178 ISBN 10: 0367574179 Pages: 227 Publication Date:30 June 2020 Audience:
College/higher education
,
General/trade
,
Primary
,
ELT Advanced
Format:Paperback Publisher's Status: Active
Preface 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 Index