Self-adaptive systems for machine intelligence:
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| Main Author: | |
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| Format: | Book |
| Language: | Undetermined |
| Published: |
Hoboken, NJ
Wiley
2011
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XVI, 230 S. zahlr. graph. Darst. |
| ISBN: | 9780470343968 0470343966 |
Staff View
MARC
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| 245 | 1 | 0 | |a Self-adaptive systems for machine intelligence |c Haibo He |
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| 300 | |a XVI, 230 S. |b zahlr. graph. Darst. | ||
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Record in the Search Index
| _version_ | 1804138542005223424 |
|---|---|
| adam_text | CONTENTS
Preface
xi
Acknowledgments
xv
1
Introduction
1
1.1
The Machine Intelligence Research
1
1.2
The Two-Fold Objectives: Data-Driven and Biologically
Inspired Approaches
4
1.3
How to Read This Book
8
1.3.1
Part I: Data-Driven Approaches for Machine
Intelligence (Chapters
2, 3,
and
4) 8
1.3.2
Part II: Biologically-Inspired Approaches for
Machine Intelligence (Chapters
4, 5,
and
6) 9
1.4
Summary and Further Reading
10
References
10
2
Incremental Learning
13
2.1
Introduction
13
2.2
Problem Foundation
13
2.3
An Adaptive Incremental Learning Framework
14
2.4
Design of the Mapping Function
19
2.4.1
Mapping Function Based on Euclidean
Distance
19
2.4.2
Mapping Function Based on Regression Learning
Model
20
2.4.3
Mapping Function Based on Online Value
System
23
2.4.3.1
A Three-Curve Fitting (TCF) Technique
23
2.4.3.2
System-Level Architecture for Online Value
Estimation
26
VI
CONTENTS
2.5
Case
Study
29
2.5.1
Incremental Learning from Video Stream
30
2.5.1.1
Feature Representation
30
2.5.1.2
Experimental Results
31
2.5.1.3
Concept Drifting Issue in Incremental
Learning
33
2.5.2
Incremental Learning for Spam E-mail
Classification
37
2.5.2.1
Data Set Characteristic and System
Configuration
37
2.5.2.2
Simulation Results
38
2.6
Summary
39
References
41
3
Imbalanced Learning
44
3.1
Introduction
44
3.2
The Nature of Imbalanced Learning
44
3.3
Solutions for Imbalanced Learning
48
3.3.1
Sampling Methods for Imbalanced Learning
49
3.3.1.1
Random Oversampling and
Undersampling
50
3.3.1.2
Informed
Undersampling
51
3.3.1.3
Synthetic Sampling with Data Generation
52
3.3.1.4
Adaptive Synthetic Sampling
53
3.3.1.5
Sampling with Data Cleaning Techniques
56
3.3.1.6
Cluster-Based Sampling Method
57
3.3.1.7
Integration of Sampling and Boosting
59
3.3.2
Cost-Sensitive Methods for Imbalanced
Learning
62
3.3.2.1
Cost-Sensitive Learning Framework
62
3.3.2.2
Cost-Sensitive Data Space Weighting with
Adaptive Boosting
63
3.3.2.3
Cost-Sensitive Decision Trees
65
3.3.2.4
Cost-Sensitive Neural Networks
66
3.3.3
Kernel-Based Methods for Imbalanced
Learning
68
3.3.3.1
Kernel-Based Learning Framework
68
3.3.3.2
Integration of Kernel Methods with Sampling
Methods
69
CONTENTS
VU
3.3.3.3 Kernel
Modification
Methods for Imbalanced
Learning
70
3.3.4
Active Learning Methods for Imbalanced
Learning
71
3.3.5
Additional Methods for Imbalanced Learning
73
3.4
Assessment Metrics for Imbalanced Learning
75
3.4.1
Singular Assessment Metrics
75
3.4.2
Receiver Operating Characteristics (ROC)
Curves
77
3.4.3
Precision-Recall (PR) Curves
79
3.4.4
Cost Curves
80
3.4.5
Assessment Metrics for Multiclass Imbalanced
Learning
80
3.5
Opportunities and Challenges
82
3.6
Case Study
84
3.6.1
Nonlinear Normalization
84
3.6.2
Data Sets Distribution
88
3.6.3
Simulation Results and Discussions
92
3.7
Summary
98
References
100
4
Ensemble Learning
108
108
108
109
110
110
110
111
111
111
112
112
114
114
114
4.1
Introduction
4.2
Hypothesis Diversity
4.2.1
g
-Statistics
4.2.2
Correlation Coefficient
4.2.3
Disagreement Measure
4.2.4
Double-Fault Measure
4.2.5
Entropy Measure
4.2.6
Kohavi-Wolpert Variance
4.2.7
Interrater
Agreement
4.2.8
Measure of Difficulty
4.2.9
Generalized Diversity
4.3
Developing Multiple Hypotheses
4.3.1
Bootstrap Aggregating
4.3.2
Adaptive Boosting
VIH
CONTENTS
4.3.3
Subspace
Learning
119
4.3.4
Stacked Generalization
120
4.3.5
Mixture of Experts
122
4.4
Integrating Multiple Hypotheses
123
4.5
Case Study
126
4.5.1
Data Sets and Experiment Configuration
127
4.5.2
Simulation Results
128
4.5.3
Margin Analysis
129
4.5.3.1
A Short History of Margin Analysis
129
4.5.3.2
Margin Analysis for Ensemble Learning
131
4.6
Summary
136
References
137
5
Adaptive Dynamic Programming for Machine Intelligence
140
5.1
Introduction
140
5.2
Fundamental Objectives: Optimization and Prediction
141
5.3
ADP for Machine Intelligence
143
5.3.1
Hierarchical Architecture in ADP Design
143
5.3.2
Learning and Adaptation in ADP
146
5.3.2.1
The Action Network
148
5.3.2.2
The Reference Network
150
5.3.2.3
The Critic Network
152
5.3.3
Learning Strategies: Sequential Learning and
Cooperative Learning
154
5.4
Case Study
155
5.5
Summary
160
References
161
6
Associative Learning
165
6.1
Introduction
165
6.2
Associative Learning Mechanism
165
6.2.1
Structure Individual Processing Elements
166
6.2.2
Self-Determination of the Function Value
167
6.2.3
Signal Strength for Associative Learning
168
CONTENTS
ІХ
6.2.4
The Associative Learning Principle
169
6.3
Associative Learning in Hierarchical Neural Networks
173
6.3.1
Network Structure
173
6.3.2
Network Operation
174
6.3.2.1
Feedforward Operation
174
6.3.2.2
Feedback Operation
177
6.4
Case Study
180
6.4.1
Hetero-
Associative Application
180
6.4.2
Auto-Associative Application
182
6.4.2.1
Panda Image Recovery
183
6.4.2.2
Chinese Character Recognition and
Recovery
184
6.4.2.3
Associative Memory for Online Incremental
Learning
186
6.5
Summary
187
References
188
Sequence Learning
190
7.1
Introduction
190
7.2
Foundations for Sequence Learning
190
7.3
Sequence Learning in Hierarchical Neural Structure
194
7.4
Level
0:
A Modified Hebbian Learning Architecture
195
7.5
Level
1
to Level N: Sequence Storage, Prediction, and
Retrieval
198
7.5.1
Sequence Storage
198
7.5.2
Sequence Prediction
201
7.5.2.1
Prediction Mechanism
201
7.5.2.2
Activation of Prediction Neuron
205
7.5.2.3
Time-Controlled Multiplexer
205
7.5.3
Sequence Retrieval
207
7.6
Memory Requirement
207
7.7
Learning and Anticipation of Multiple Sequences
208
7.8
CaseStudy
211
7.9
Summary
212
References
213
X
CONTENTS
8 Hardware Design
for Machine Intelligence
217
8.1
A Final Comment
217
References
220
List of Abbreviations
222
Index
227
|
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| indexdate | 2024-07-09T21:29:50Z |
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| isbn | 9780470343968 0470343966 |
| language | Undetermined |
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| owner | DE-473 DE-BY-UBG DE-12 |
| owner_facet | DE-473 DE-BY-UBG DE-12 |
| physical | XVI, 230 S. zahlr. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
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| publisher | Wiley |
| record_format | marc |
| spelling | Haibo, He Verfasser (DE-588)1014438217 aut Self-adaptive systems for machine intelligence Haibo He Hoboken, NJ Wiley 2011 XVI, 230 S. zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Computerarchitektur (DE-588)4048717-9 gnd rswk-swf Adaptives System (DE-588)4247928-9 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Adaptives System (DE-588)4247928-9 s Computerarchitektur (DE-588)4048717-9 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017065163&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Haibo, He Self-adaptive systems for machine intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd Computerarchitektur (DE-588)4048717-9 gnd Adaptives System (DE-588)4247928-9 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4048717-9 (DE-588)4247928-9 |
| title | Self-adaptive systems for machine intelligence |
| title_auth | Self-adaptive systems for machine intelligence |
| title_exact_search | Self-adaptive systems for machine intelligence |
| title_full | Self-adaptive systems for machine intelligence Haibo He |
| title_fullStr | Self-adaptive systems for machine intelligence Haibo He |
| title_full_unstemmed | Self-adaptive systems for machine intelligence Haibo He |
| title_short | Self-adaptive systems for machine intelligence |
| title_sort | self adaptive systems for machine intelligence |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Computerarchitektur (DE-588)4048717-9 gnd Adaptives System (DE-588)4247928-9 gnd |
| topic_facet | Künstliche Intelligenz Computerarchitektur Adaptives System |
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