Internet-scale pattern recognition: new techniques for voluminous data sets and data clouds
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
CRC Press
2013
|
| Series: | A Chapman & Hall book
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XVIII, 179 S. Ill. graph. Darst. |
| ISBN: | 9781466510968 |
Staff View
MARC
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| 245 | 1 | 0 | |a Internet-scale pattern recognition |b new techniques for voluminous data sets and data clouds |c Anang Hudaya Muhamad Amin ; Asad I. Khan ; Benny B. Nasution |
| 246 | 1 | 3 | |a Internet scale pattern recognition |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2013 | |
| 300 | |a XVIII, 179 S. |b Ill. graph. Darst. | ||
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Record in the Search Index
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| adam_text |
Contents
Preface
xi
Acknowledgments
xv
About the Authors
xvii
I Recognition: A New Perspective
1
1
Introduction
3
1.1
As We See, We Learn
. 3
1.2
Recognition at a Large Scale
. 4
1.3
Computational Intelligence Approach for Pattern Recognition
8
1.4
Scalability in Pattern Recognition
. 11
1.4.1
Common Barriers
. 11
1.4.2
Possible Solutions
. 12
1.4.3
Distributed Computing Solution for Scalability of PR
Schemes
. 13
2
Distributed Approach for Pattern Recognition
15
2.1
Scalability of Neural Network Approaches
. 16
2.1.1
Pattern Storage Capacity
. 16
2.1.2
Inter-Neuron Communication Frequency
. 17
2.2
Key Components of DPR
. 20
2.2.1
Learning Mechanism
. 20
2.2.2
Processing Approach
. 21
2.2.3
Training Procedure
. 21
2.3
System Approaches
. 22
2.4
Pattern Distribution Techniques
. 25
2.4.1
Subpattern Distribution
. 25
2.4.2
Pattern Set Distribution
. 26
2.5
Current DPR Schemes
. 27
2.5.1
Graph Neuron
. 27
2.5.2
Hierarchical Graph Neuron
. 29
2.5.3
Distributed Hierarchical Graph Neuron
. 30
vu
viii Contents
2.6
Resource
Considerations for DPR Implementations
. 30
2.6.1
Resource-Aware Approach
. 31
2.6.2
Message-Passing Model in DPR
. 31
II Evolution of Internet-Scale Recognition
33
3
One-Shot Learning Considerations
35
3.1
One-Shot Learning Graph Neuron (GN) Scheme
. 36
3.1.1
Pattern Representation
. 37
3.1.2
Recognition Procedure
. 38
3.2
One-Shot Learning Model
. 41
3.2.1
Bias Array Design for Pattern Memorization
. 42
3.2.2
Collaborative-Comparison Learning Technique
. 42
3.3
GN Complexity Estimation
. 44
3.4
Graph Neuron Limitations
. 46
3.5
Significance of One-Shot Learning
. 48
4
Hierarchical Model for Pattern Recognition
49
4.1
Evolution of
О
ne-S
hot Learning: The Hierarchical Approach
49
4.1.1
Solution to Crosstalk Problem
. 51
4.1.2
Computational Design for a Hierarchical One-Shot
Learning DPR Scheme
. 52
4.1.3
HGN Recognition Procedure
. 55
4.2
Complexity and Scalability of Hierarchical DPR Scheme
. . 57
4.2.1
Complexity Estimation
. 57
4.2.2
Scalability in HGN Approach
. 60
4.3
Reducing Hierarchical Complexity: A Distributed Approach
60
4.3.1
Distributed Neurons of HGN Network
. 61
4.3.2
Distributed HGN Approach
. 63
4.4
Design Evaluation for Distributed DPR Approach
. 65
4.4.1
Non-Uniform Distribution
. 65
4.4.2
Uniform Distribution
. 69
5
Recognition via Divide-and-Distribute Approach
73
5.1
Divide-and-Distribute Approach for One-Shot Learning IS-PR
Scheme
.,. 73
5.1.1
Associative Memory (AM) Concept in Pattern Recog¬
nition
. 74
5.1.2
DHGN Computational Design
. 75
5.1.3
Dual-Phase Recognition Procedure
. 80
Contents ix
5.2
Dimensionality Reduction in Pattern Pre-Processing
. 87
5.2.1
Structural Reduction
. 87
5.2.2
Content Reduction
. 88
5.3
Remarks on DHGN DPR Scheme
. 89
III Systems and Tools
91
6
Internet-Scale Applications Development
93
6.1
Distributed Computing Models for IS-PR
. 93
6.1.1
Commodity Grid (CoG)
. 94
6.1.2
Cloud Computing
. 94
6.1.3
Peer-to-Peer (P2P) Computing
. 98
6.2
Parallel Programming Techniques
. 100
6.2.1
Message-Passing Scheme
. 100
6.2.2
GPU Programming
. 103
6.3
From Coding to Applications
. 104
IV Implementations and Applications
107
7
M
ulti-
Feat
ure
Classifications for Complex Data
109
7.1
Data Features for Pattern Recognition
. 110
7.2
Distributed
Multi-
Feature Recognition
.
Ill
7.2.1
Conceptual Design and Implementation
. 112
7.2.2
Complexity Estimation
. 113
7.3
Handwritten Object Classification with Multiple Features
. . 116
7.3.1
Handwritten Object
. 117
7.3.2
Classification Procedures
. 118
7.4
Distributed Multi-Feature Recognition Perspective
. 120
8
Pattern Recognition within Coarse-Grained Networks
121
8.1
Network Granularity Considerations
. 121
8.1.1
DHGN Configurations for Adaptive Granularity
. 122
8.1.2
DHGN Commodity Grid Framework
. 124
8.2
Face Recognition Using the Multi-Feature DPR Approach
. 128
8.2.1
Color and
Spatio-Structural
Features Consideration
. 129
8.3
Distributed Data Management within Cloud Computing
. . 132
8.3.1
Cloud Data Access Scheme
. 133
8.3.2
DHGN Approach for Cloud Data Access
. 135
8.4
Adaptive Recognition: A Different Perspective
. 138
χ
Contents
9
Event Detection within Fine-Grained Networks
139
9.1
Distributed Event Detection Scheme for Wireless Sensor
Networks
. 139
9.1.1
WSN Event Detection
. 140
9.1.2
DHGN-WSN Event Detection Configuration
. 141
9.1.3
Dimensionality Reduction in Sensory Data
. 143
9.1.4
Event Classification
. 144
9.1.5
Performance Metrics: Memory Utilization
. 145
9.1.6
Spatio-
Temporal Analysis of Event Data
. 146
9.2
Integrated Grid-Sensor Scheme for Structural Analysis
. 148
9.2.1
Integrated Grid-Sensor Network Framework for Struc¬
tural Engineering
. 150
9.2.2
Structural Analysis, Design, and Monitoring
Applications
. 151
9.3
Distributed Event Detection: A Lightweight Approach
. 156
V The Way Forward
159
10
Recognition: The Future and Beyond
161
10.1
Medium of Change
. 161
10.2
Future of Internet-Scale PR
. 162
10.3
Making a Case
. 163
10.3.1
Changing the Fundamentals
. 164
10.3.2
Recognition as Commodity
. 165
Bibliography
167
Index
177
For machine intelligence applications to work successfully,
machines must perform reliably under variations of data and must
be able to keep up with data streams. Internet-Scale Pattern
Recognition: New Techniques for Voluminous Data Sets
and Data Clouds unveils computational models that address
performance and scalability to achieve higher levels of reliability.
It explores different ways of implementing pattern recognition
using machine intelligence.
Based on the authors' research from the past
10
years, the text
draws on concepts from pattern recognition, parallel processing,
distributed systems, and data networks. It describes fundamental
research on the scalability and performance of pattern recognition,
addressing issues with existing pattern recognition schemes for
Internet-scale data deployment. The authors review numerous
approaches and introduce possible solutions to the scalability
problem.
By presenting the concise body of knowledge required for
reliable and scalable pattern recognition, this book shortens the
learning curve and gives you valuable insight to make further
innovations. It offers an extendable template for Internet-scale
pattern recognition applications as well as guidance on the
programming of large networks of devices. |
| any_adam_object | 1 |
| author | Muhamad Amin, Anang Hudaya Khan, Asad I. Nasution, Benny B. |
| author_GND | (DE-588)1035315629 (DE-588)1035315963 |
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| id | DE-604.BV041349624 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T00:16:02Z |
| institution | BVB |
| isbn | 9781466510968 |
| language | English |
| lccn | 2012031622 |
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| physical | XVIII, 179 S. Ill. graph. Darst. |
| publishDate | 2013 |
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| publisher | CRC Press |
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| spelling | Muhamad Amin, Anang Hudaya Verfasser (DE-588)1035315629 aut Internet-scale pattern recognition new techniques for voluminous data sets and data clouds Anang Hudaya Muhamad Amin ; Asad I. Khan ; Benny B. Nasution Internet scale pattern recognition Boca Raton [u.a.] CRC Press 2013 XVIII, 179 S. Ill. graph. Darst. txt rdacontent n rdamedia nc rdacarrier A Chapman & Hall book Datenverarbeitung Data mining Pattern recognition systems Web usage mining Mathematical statistics Data processing COMPUTERS / Database Management / Data Mining bisacsh COMPUTERS / Machine Theory bisacsh COMPUTERS / Internet / General bisacsh Data Mining (DE-588)4428654-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Mustererkennung (DE-588)4040936-3 s Data Mining (DE-588)4428654-5 s DE-604 Khan, Asad I. Verfasser aut Nasution, Benny B. Verfasser (DE-588)1035315963 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026798225&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026798225&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Muhamad Amin, Anang Hudaya Khan, Asad I. Nasution, Benny B. Internet-scale pattern recognition new techniques for voluminous data sets and data clouds Datenverarbeitung Data mining Pattern recognition systems Web usage mining Mathematical statistics Data processing COMPUTERS / Database Management / Data Mining bisacsh COMPUTERS / Machine Theory bisacsh COMPUTERS / Internet / General bisacsh Data Mining (DE-588)4428654-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4226127-2 (DE-588)4040936-3 |
| title | Internet-scale pattern recognition new techniques for voluminous data sets and data clouds |
| title_alt | Internet scale pattern recognition |
| title_auth | Internet-scale pattern recognition new techniques for voluminous data sets and data clouds |
| title_exact_search | Internet-scale pattern recognition new techniques for voluminous data sets and data clouds |
| title_full | Internet-scale pattern recognition new techniques for voluminous data sets and data clouds Anang Hudaya Muhamad Amin ; Asad I. Khan ; Benny B. Nasution |
| title_fullStr | Internet-scale pattern recognition new techniques for voluminous data sets and data clouds Anang Hudaya Muhamad Amin ; Asad I. Khan ; Benny B. Nasution |
| title_full_unstemmed | Internet-scale pattern recognition new techniques for voluminous data sets and data clouds Anang Hudaya Muhamad Amin ; Asad I. Khan ; Benny B. Nasution |
| title_short | Internet-scale pattern recognition |
| title_sort | internet scale pattern recognition new techniques for voluminous data sets and data clouds |
| title_sub | new techniques for voluminous data sets and data clouds |
| topic | Datenverarbeitung Data mining Pattern recognition systems Web usage mining Mathematical statistics Data processing COMPUTERS / Database Management / Data Mining bisacsh COMPUTERS / Machine Theory bisacsh COMPUTERS / Internet / General bisacsh Data Mining (DE-588)4428654-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Datenverarbeitung Data mining Pattern recognition systems Web usage mining Mathematical statistics Data processing COMPUTERS / Database Management / Data Mining COMPUTERS / Machine Theory COMPUTERS / Internet / General Data Mining Neuronales Netz Mustererkennung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026798225&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026798225&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT muhamadaminananghudaya internetscalepatternrecognitionnewtechniquesforvoluminousdatasetsanddataclouds AT khanasadi internetscalepatternrecognitionnewtechniquesforvoluminousdatasetsanddataclouds AT nasutionbennyb internetscalepatternrecognitionnewtechniquesforvoluminousdatasetsanddataclouds AT muhamadaminananghudaya internetscalepatternrecognition AT khanasadi internetscalepatternrecognition AT nasutionbennyb internetscalepatternrecognition |