Learning with Uncertainty

Xizhao Wang, Junhai Zhai



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.

Features

• Focuses on learning with uncertainty (first comprehensive book to do so)
• Covers the main branches of machine learning, including inductive learning, unsupervised learning, active learning and ensemble learning
• Provides many examples to help readers understand the impact of uncertainty on learning
• Includes hot research topics, such as learning from big data with uncertainty
• Offers latest and valuable literature



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