Learning with Uncertainty

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A01=Junhai Zhai
A01=Xizhao Wang
Active learning
Age Group_Uncategorized
Age Group_Uncategorized
Author_Junhai Zhai
Author_Xizhao Wang
automatic-update
Category1=Non-Fiction
Category=PBT
Category=UYQM
Clustering
computational intelligence
COP=United States
Data Set
DE Algorithm
DE Strategy
Decision tree learning
Delivery_Delivery within 10-20 working days
Ensemble learning
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
FCM Algorithm
Feature Weight
Fuzzy Attribute
Fuzzy Clustering
Fuzzy Decision
Fuzzy Decision Tree
Fuzzy Entropy
Fuzzy ID3
Fuzzy Integral
Fuzzy Linguistic Terms
Fuzzy Partition
Fuzzy Rough Set
Fuzzy Set
graduate level textbook
Interclass Similarity
Intraclass Similarity
Language_English
Learned Decision Tree
Machine learning
machine learning theory
Misclassified Samples
PA=Available
pattern recognition methods
Pool Tree
Price_€100 and above
probabilistic modeling
PS=Active
Random Selection Method
softlaunch
UCI Data Set
Uncertainty
uncertainty in artificial intelligence
uncertainty quantification
Unlabeled Instances

Product details

  • ISBN 9781498724128
  • Weight: 504g
  • Dimensions: 178 x 254mm
  • Publication Date: 16 Nov 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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.

Xizhao Wang, Junhai Zhai

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