Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Image from Google Jackets

Internet-scale pattern recognition : new techniques for voluminous data sets and data clouds / Anang Hudaya Muhamad Amin, Asad I. Khan and Benny B. Nasution.

By: Contributor(s): Material type: TextTextPublication details: Boca Raton : CRC Press, c2013.Description: xviii, 179 p. : ill. ; 24 cmISBN:
  • 9781466510968 (hardback)
Subject(s): DDC classification:
  • 006.312 23 M952
Contents:
Preface -- Acknowledgments -- About the Authors -- I. Recognition: A New Perspective -- 1. Introduction -- 1.1. As We See, We Learn -- 1.2. Recognition at a Large Scale -- 1.3. Computational Intelligence Approach for Pattern Recognition -- 1.4. Scalability in Pattern Recognition -- 1.4.1. Common Barriers -- 1.4.2. Possible Solutions -- 1.4.3. Distributed Computing Solution for Scalability of PR Schemes -- 2. Distributed Approach for Pattern Recognition -- 2.1. Scalability of Neural Network Approaches -- 2.1.1. Pattern Storage Capacity -- 2.1.2. Inter-Neuron Communication Frequency -- 2.2. Key Components of DPR -- 2.2.1. Learning Mechanism -- 2.2.2. Processing Approach -- 2.2.3. Training Procedure -- 2.3. System Approaches -- 2.4. Pattern Distribution Techniques -- 2.4.1. Subpattern Distribution -- 2.4.2. Pattern Set Distribution -- 2.5. Current DPR Schemes -- 2.5.1. Graph Neuron -- 2.5.2. Hierarchical Graph Neuron -- 2.5.3. Distributed Hierarchical Graph Neuron -- 2.6. Resource Considerations for DPR Implementations -- 2.6.1. Resource-Aware Approach -- 2.6.2. Message-Passing Model in DPR -- II. Evolution of Internet-Scale Recognition -- 3. One-Shot Learning Considerations -- 3.1. One-Shot Learning Graph Neuron (GN) Scheme -- 3.1.1. Pattern Representation -- 3.1.2. Recognition Procedure -- 3.2. One-Shot Learning Model -- 3.2.1. Bias Array Design for Pattern Memorization -- 3.2.2. Collaborative-Comparison Learning Technique -- 3.3. GN Complexity Estimation -- 3.4. Graph Neuron Limitations -- 3.5. Significance of One-Shot Learning -- 4. Hierarchical Model for Pattern Recognition -- 4.1. Evolution of One-Shot Learning: The Hierarchical Approach -- 4.1.1. Solution to Crosstalk Problem -- 4.1.2. Computational Design for a Hierarchical One-Shot Learning DPR Scheme -- 4.1.3. HGN Recognition Procedure -- 4.2. Complexity and Scalability of Hierarchical DPR Scheme -- 4.2.1. Complexity Estimation -- 4.2.2. Scalability in HGN Approach -- 4.3. Reducing Hierarchical Complexity: A Distributed Approach -- 4.3.1. Distributed Neurons of HGN Network -- 4.3.2. Distributed HGN Approach -- 4.4. Design Evaluation for Distributed DPR Approach -- 4.4.1. Non-Uniform Distribution -- 4.4.2. Uniform Distribution -- 5. Recognition via Divide-and-Distribute Approach -- 5.1. Divide-and-Distribute Approach for One-Shot Learning S-PR Scheme -- 5.1.1. Associative Memory (AM) Concept in Pattern Recognition -- 5.1.2. DHGN Computational Design -- 5.1.3. Dual-Phase Recognition Procedure -- 5.2. Dimensionality Reduction in Pattern Pre-Processing -- 5.2.1. Structural Reduction -- 5.2.2. Content Reduction -- 5.3. Remarks on DHGN DPR Scheme -- III. Systems and Tools -- 6. Internet-Scale Applications Development -- 6.1. Distributed Computing Models for IS-PR -- 6.1.1. Commodity Grid (CoG) -- 6.1.2. Cloud Computing -- 6.1.3. Peer-to-Peer (P2P) Computing -- 6.2. Parallel Programming Techniques -- 6.2.1. Message-Passing Scheme -- 6.2.2. GPU Programming -- 6.3. From Coding to Applications -- IV. Implementations and Applications -- 7. Multi-Feature Classifications for Complex Data -- 7.1. Data Features for Pattern Recognition -- 7.2. Distributed Multi-Feature Recognition -- 7.2.1. Conceptual Design and Implementation -- 7.2.2. Complexity Estimation -- 7.3. Handwritten Object Classification with Multiple Features -- 7.3.1. Handwritten Object -- 7.3.2. Classification Procedures -- 7.4. Distributed Multi-Feature Recognition Perspective -- 8. Pattern Recognition within Coarse-Grained Networks -- 8.1. Network Granularity Considerations -- 8.1.1. DHGN Configurations for Adaptive Granularity -- 8.1.2. DHGN Commodity Grid Framework -- 8.2. Face Recognition Using the Multi-Feature DPR Approach -- 8.2.1. Color and Spatio-Structural Features Consideration -- 8.3. Distributed Data Management within Cloud Computing -- 8.3.1. Cloud Data Access Scheme -- 8.3.2. DHGN Approach for Cloud Data Access -- 8.4. Adaptive Recognition: A Different Perspective -- 9. Event Detection within Pine-Grained Networks -- 9.1. Distributed Event Detection Scheme for Wireless Sensor Networks -- 9.1.1. WSN Event Detection -- 9.1.2. DHGN-WSN Event Detection Configuration -- 9.1.3. Dimensionality Reduction in Sensory Data -- 9.1.4. Event Classification -- 9.1.5. Performance Metrics: Memory Utilization -- 9.1.6. Spatio-Temporal Analysis of Event Data -- 9.2. Integrated Grid-Sensor Scheme for Structural Analysis -- 9.2.1. Integrated Grid-Sensor Network Framework for Structural Engineering -- 9.2.2. Structural Analysis, Design, and Monitoring Applications -- 9.3. Distributed Event Detection: A Lightweight Approach -- V. The Way Forward -- 10. Recognition: The Future and Beyond -- 10.1. Medium of Change -- 10.2. Future of Internet-Scale PR -- 10.3. Making a Case -- 10.3.1. Changing the Fundamentals -- 10.3.2. Recognition as Commodity -- Bibliography -- Index.
Summary: "This cutting-edge reference outlines the underlying theory and principles of efficient and effective distributed pattern recognition involving one-shot learning and in-network processing for different types of applications, including multimedia retrieval systems and event detection over different network environments. Investigating one-shot learning and in-network processing as complementary mechanisms for efficient and accurate distributed pattern analyses, it presents the technical aspects related to the development of scalable pattern recognition using a number of contemporary application development tools. It also considers scalability of pattern recognition schemes when dealing with such data"--
Tags from this library: No tags from this library for this title. Log in to add tags.

Includes bibliographical references (p. 167-175) and index.

Preface --
Acknowledgments --
About the Authors --
I. Recognition: A New Perspective --
1. Introduction --
1.1. As We See, We Learn --
1.2. Recognition at a Large Scale --
1.3. Computational Intelligence Approach for Pattern Recognition --
1.4. Scalability in Pattern Recognition --
1.4.1. Common Barriers --
1.4.2. Possible Solutions --
1.4.3. Distributed Computing Solution for Scalability of PR Schemes --
2. Distributed Approach for Pattern Recognition --
2.1. Scalability of Neural Network Approaches --
2.1.1. Pattern Storage Capacity --
2.1.2. Inter-Neuron Communication Frequency --
2.2. Key Components of DPR --
2.2.1. Learning Mechanism --
2.2.2. Processing Approach --
2.2.3. Training Procedure --
2.3. System Approaches --
2.4. Pattern Distribution Techniques --
2.4.1. Subpattern Distribution --
2.4.2. Pattern Set Distribution --
2.5. Current DPR Schemes --
2.5.1. Graph Neuron --
2.5.2. Hierarchical Graph Neuron --
2.5.3. Distributed Hierarchical Graph Neuron --
2.6. Resource Considerations for DPR Implementations --
2.6.1. Resource-Aware Approach --
2.6.2. Message-Passing Model in DPR --
II. Evolution of Internet-Scale Recognition --
3. One-Shot Learning Considerations --
3.1. One-Shot Learning Graph Neuron (GN) Scheme --
3.1.1. Pattern Representation --
3.1.2. Recognition Procedure --
3.2. One-Shot Learning Model --
3.2.1. Bias Array Design for Pattern Memorization --
3.2.2. Collaborative-Comparison Learning Technique --
3.3. GN Complexity Estimation --
3.4. Graph Neuron Limitations --
3.5. Significance of One-Shot Learning --
4. Hierarchical Model for Pattern Recognition --
4.1. Evolution of One-Shot Learning: The Hierarchical Approach --
4.1.1. Solution to Crosstalk Problem --
4.1.2. Computational Design for a Hierarchical One-Shot Learning DPR Scheme --
4.1.3. HGN Recognition Procedure --
4.2. Complexity and Scalability of Hierarchical DPR Scheme --
4.2.1. Complexity Estimation --
4.2.2. Scalability in HGN Approach --
4.3. Reducing Hierarchical Complexity: A Distributed Approach --
4.3.1. Distributed Neurons of HGN Network --
4.3.2. Distributed HGN Approach --
4.4. Design Evaluation for Distributed DPR Approach --
4.4.1. Non-Uniform Distribution --
4.4.2. Uniform Distribution --
5. Recognition via Divide-and-Distribute Approach --
5.1. Divide-and-Distribute Approach for One-Shot Learning
S-PR Scheme --
5.1.1. Associative Memory (AM) Concept in Pattern Recognition --
5.1.2. DHGN Computational Design --
5.1.3. Dual-Phase Recognition Procedure --
5.2. Dimensionality Reduction in Pattern Pre-Processing --
5.2.1. Structural Reduction --
5.2.2. Content Reduction --
5.3. Remarks on DHGN DPR Scheme --
III. Systems and Tools --
6. Internet-Scale Applications Development --
6.1. Distributed Computing Models for IS-PR --
6.1.1. Commodity Grid (CoG) --
6.1.2. Cloud Computing --
6.1.3. Peer-to-Peer (P2P) Computing --
6.2. Parallel Programming Techniques --
6.2.1. Message-Passing Scheme --
6.2.2. GPU Programming --
6.3. From Coding to Applications --
IV. Implementations and Applications --
7. Multi-Feature Classifications for Complex Data --
7.1. Data Features for Pattern Recognition --
7.2. Distributed Multi-Feature Recognition --
7.2.1. Conceptual Design and Implementation --
7.2.2. Complexity Estimation --
7.3. Handwritten Object Classification with Multiple Features --
7.3.1. Handwritten Object --
7.3.2. Classification Procedures --
7.4. Distributed Multi-Feature Recognition Perspective --
8. Pattern Recognition within Coarse-Grained Networks --
8.1. Network Granularity Considerations --
8.1.1. DHGN Configurations for Adaptive Granularity --
8.1.2. DHGN Commodity Grid Framework --
8.2. Face Recognition Using the Multi-Feature DPR Approach --
8.2.1. Color and Spatio-Structural Features Consideration --
8.3. Distributed Data Management within Cloud Computing --
8.3.1. Cloud Data Access Scheme --
8.3.2. DHGN Approach for Cloud Data Access --
8.4. Adaptive Recognition: A Different Perspective --
9. Event Detection within Pine-Grained Networks --
9.1. Distributed Event Detection Scheme for Wireless Sensor Networks --
9.1.1. WSN Event Detection --
9.1.2. DHGN-WSN Event Detection Configuration --
9.1.3. Dimensionality Reduction in Sensory Data --
9.1.4. Event Classification --
9.1.5. Performance Metrics: Memory Utilization --
9.1.6. Spatio-Temporal Analysis of Event Data --
9.2. Integrated Grid-Sensor Scheme for Structural Analysis --
9.2.1. Integrated Grid-Sensor Network Framework for Structural Engineering --
9.2.2. Structural Analysis, Design, and Monitoring Applications --
9.3. Distributed Event Detection: A Lightweight Approach --
V. The Way Forward --
10. Recognition: The Future and Beyond --
10.1. Medium of Change --
10.2. Future of Internet-Scale PR --
10.3. Making a Case --
10.3.1. Changing the Fundamentals --
10.3.2. Recognition as Commodity --
Bibliography --
Index.

"This cutting-edge reference outlines the underlying theory and principles of efficient and effective distributed pattern recognition involving one-shot learning and in-network processing for different types of applications, including multimedia retrieval systems and event detection over different network environments. Investigating one-shot learning and in-network processing as complementary mechanisms for efficient and accurate distributed pattern analyses, it presents the technical aspects related to the development of scalable pattern recognition using a number of contemporary application development tools. It also considers scalability of pattern recognition schemes when dealing with such data"--

There are no comments on this title.

to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in