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E-raamat: Learning with Uncertainty

(Hebei University, Baoding, PR of China), (Hebei University, Baoding, PR of China)
  • Formaat: 239 pages
  • Ilmumisaeg: 25-Nov-2016
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781498724135
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Learning with UncertaintySuurem pilt
  • Formaat: 239 pages
  • Ilmumisaeg: 25-Nov-2016
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781498724135
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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.
Preface ix
Symbols and Abbreviations xi
1 Uncertainty
1(16)
1.1 Randomness
1(4)
1.1.1 Entropy
1(2)
1.1.2 Joint Entropy and Conditional Entropy
3(1)
1.1.3 Mutual Information
4(1)
1.2 Fuzziness
5(4)
1.2.1 Definition and Representation of Fuzzy Sets
6(1)
1.2.2 Basic Operations and Properties of Fuzzy Sets
7(1)
1.2.3 Fuzzy Measures
8(1)
1.3 Roughness
9(1)
1.4 Nonspecificity
10(1)
1.5 Relationships among the Uncertainties
11(6)
1.5.1 Entropy and Fuzziness
12(2)
1.5.2 Fuzziness and Ambiguity
14(1)
References
15(2)
2 Decision Tree with Uncertainty
17(42)
2.1 Crisp Decision Tree
17(11)
2.1.1 ID3 Algorithm
18(4)
2.1.2 Continuous-Valued Attributes Decision Trees
22(6)
2.2 Fuzzy Decision Tree
28(9)
2.3 Fuzzy Decision Tree Based on Fuzzy Rough Set Techniques
37(7)
2.3.1 Fuzzy Rough Sets
37(3)
2.3.2 Generating Fuzzy Decision Tree with Fuzzy Rough Set Technique
40(4)
2.4 Improving Generalization of Fuzzy Decision Tree by Maximizing Fuzzy Entropy
44(15)
2.4.1 Basic Idea of Refinement
44(1)
2.4.2 Globally Weighted Fuzzy If-Then Rule Reasoning
44(5)
2.4.3 Refinement Approach to Updating the Parameters
49(1)
2.4.3.1 Maximum Fuzzy Entropy Principle
50(5)
References
55(4)
3 Clustering under Uncertainty Environment
59(40)
3.1 Introduction
59(1)
3.2 Clustering Algorithms Based on Hierarchy or Partition
60(10)
3.2.1 Clustering Algorithms Based on Hierarchy
60(6)
3.2.2 Clustering Algorithms Based on Partition
66(4)
3.3 Validation Functions of Clustering
70(1)
3.4 Feature Weighted Fuzzy Clustering
71(2)
3.5 Weighted Fuzzy Clustering Based on Differential Evolution
73(7)
3.5.1 Differential Evolution and Dynamic Differential Evolution
73(1)
3.5.1.1 Basic Differential Evolution Algorithm
73(4)
3.5.1.2 Dynamic Differential Evolution Algorithm
77(1)
3.5.2 Hybrid Differential Evolution Algorithm Based on Coevolution with Multi-Differential Evolution Strategy
78(2)
3.6 Feature Weight Fuzzy Clustering Learning Model Based on MEHDE
80(15)
3.6.1 MEHDE-Based Feature Weight Learning: MEHDE-FWL
81(1)
3.6.2 Experimental Analysis
82(2)
3.6.2.1 Comparison between MEHDE-FWL and GD-FWL Based on FCM
84(4)
3.6.2.2 Comparisons Based on SMTC Clustering
88(2)
3.6.2.3 Efficiency Analysis of GD-, DE-, DDE-, and MEHDE-Based Searching Techniques
90(5)
3.7 Summary
95(4)
References
96(3)
4 Active Learning with Uncertainty
99(50)
4.1 Introduction to Active Learning
99(3)
4.2 Uncertainty Sampling and Query-by-Committee Sampling
102(3)
4.2.1 Uncertainty Sampling
102(1)
4.2.1.1 Least Confident Rule
102(1)
4.2.1.2 Minimal Margin Rule
103(1)
4.2.1.3 Maximal Entropy Rule
103(1)
4.2.2 Query-by-Committee Sampling
103(2)
4.3 Maximum Ambiguity--Based Active Learning
105(15)
4.3.1 Some Concepts of Fuzzy Decision Tree
106(1)
4.3.2 Analysis on Samples with Maximal Ambiguity
107(2)
4.3.3 Maximum Ambiguity--Based Sample Selection
109(2)
4.3.4 Experimental Results
111(9)
4.4 Active Learning Approach to Support Vector Machine
120(29)
4.4.1 Support Vector Machine
122(1)
4.4.2 SVM Active Learning
123(1)
4.4.3 Semisupervised SVM Batch Mode Active Learning
124(1)
4.4.4 IALPSVM: An Informative Active Learning Approach to SVM
125(3)
4.4.5 Experimental Results and Discussions
128(1)
4.4.5.1 Experiments on an Artificial Data Set by Selecting a Single Query Each Time
128(3)
4.4.5.2 Experiments on Three UCI Data Sets by Selecting a Single Query Each Time
131(5)
4.4.5.3 Experiments on Two Image Data Sets by Selecting a Batch of Queries Each Time
136(10)
References
146(3)
5 Ensemble Learning with Uncertainty
149(72)
5.1 Introduction to Ensemble Learning
149(4)
5.1.1 Majority Voting and Weighted Majority Voting
150(1)
5.1.2 Approach Based on Dempster--Shafer Theory of Evidence
151(1)
5.1.3 Fuzzy Integral Ensemble Approach
152(1)
5.2 Bagging and Boosting
153(1)
5.2.1 Bagging Algorithm
153(1)
5.2.2 Boosting Algorithm
154(1)
5.3 Multiple Fuzzy Decision Tree Algorithm
154(16)
5.3.1 Induction of Multiple Fuzzy Decision Tree
155(13)
5.3.2 Experiment on Real Data Set
168(2)
5.4 Fusion of Classifiers Based on Upper Integral
170(16)
5.4.1 Extreme Learning Machine
170(2)
5.4.2 Multiple Classifier Fusion Based on Upper Integrals
172(1)
5.4.2.1 Upper Integral and Its Properties
173(2)
5.4.2.2 A Model of Classifier Fusion Based on Upper Integral
175(4)
5.4.2.3 Experimental Results
179(7)
5.5 Relationship between Fuzziness and Generalization in Ensemble Learning
186(35)
5.5.1 Classification Boundary
186(1)
5.5.1.1 Boundary and Its Estimation Given by a Learned Classifier
186(2)
5.5.1.2 Two Types of Methods for Training a Classifier
188(1)
5.5.1.3 Side Effect of Boundary and Experimental Verification
189(3)
5.5.2 Fuzziness of Classifiers
192(1)
5.5.2.1 Fuzziness of Classifier
193(1)
5.5.2.2 Relationship between Fuzziness and Misclassification
193(2)
5.5.2.3 Relationship between Fuzziness and Classification Boundary
195(3)
5.5.2.4 Divide and Conquer Strategy
198(1)
5.5.2.5 Impact of the Weighting Exponent m on the Fuzziness of Fuzzy K-NN Classifier
198(1)
5.5.3 Relationship between Generalization and Fuzziness
199(1)
5.5.3.1 Definition of Generalization and Its Elaboration
199(2)
5.5.3.2 Classifier Selection
201(1)
5.5.3.3 Explanation Based on Extreme (max/min) Fuzziness
202(3)
5.5.3.4 Experimental Results
205(11)
References
216(5)
Index 221
Xizhao Wang, Junhai Zhai
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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