Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds

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Features

  • Covers the key technologies that contribute to Internet-scale pattern recognition, including distributed systems, parallel computing, and machine intelligence
  • Outlines the underlying theory and principles of distributed pattern recognition
  • Discusses one-shot learning and hierarchical approaches in distributed pattern recognition applications
  • Includes examples of distributed models and parallel programming techniques—two forces driving the expansion of distributed applications in Internet-scale environments
  • Shows how pattern recognition can be a scalable commodity for information processing

Summary

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.

Table of Contents

I Recognition: A New Perspective
Introduction
As We See, We Learn
Recognition at a Large Scale
Computational Intelligence Approach for Pattern Recognition
Scalability in Pattern Recognition

Distributed Approach for Pattern Recognition
Scalability of Neural Network Approaches
Key Components of DPR
System Approaches
Pattern Distribution Techniques
Current DPR Schemes
Resource Considerations for DPR Implementations

II Evolution of Internet-Scale Recognition
One-Shot Learning Considerations
One-Shot Learning Graph Neuron (GN) Scheme
One-Shot Learning Model
GN Complexity Estimation
Graph Neuron Limitations
Significance of One-Shot Learning

Hierarchical Model for Pattern Recognition
Evolution of One-Shot Learning: The Hierarchical Approach
Complexity and Scalability of A Hierarchical DPR Scheme
Reducing Hierarchical Complexity: A Distributed Approach
Design Evaluation for Distributed DPR Approach

Recognition via a Divide-and-Distribute Approach
Divide-and-Distribute Approach for One-Shot Learning IS-PR Scheme
Dimensionality Reduction in Pattern Pre-Processing
Remarks on DHGN DPR Scheme

III Systems and Tools
Internet-Scale Applications Development
Distributed Computing Models for IS-PR
Parallel Programming Techniques
From Coding to Applications

IV Implementations and Applications
Multi-Feature Classifications for Complex Data
Data Features for Pattern Recognition
Distributed Multi-Feature Recognition
Handwritten Object Classification with Multiple Features
Distributed Multi-Feature Recognition Perspective

Pattern Recognition within Coarse-Grained Networks
Network Granularity Considerations
Face Recognition using the Multi-Feature DPR Approach
Distributed Data Management within Cloud Computing
Adaptive Recognition: A Different Perspective

Event Detection within Fine-Grained Networks
Distributed Event Detection Scheme for Wireless Sensor Networks
Integrated Grid-Sensor Scheme for Structural Analysis
Distributed Event Detection: A Lightweight Approach

Recognition: The Future and Beyond
Medium of Change
Future of Internet-Scale PR
Making a Case

Bibliography

Author Bio(s)

Anang Hudaya Muhamad Amin is a senior lecturer in the Faculty of Information Science and Technology at Multimedia University in Malaysia. He received a BTech (Hons.) in information technology from Universiti Teknologi PETRONAS and a masters in network computing and PhD from Monash University. His research interests include artificial intelligence with specialization in distributed pattern recognition and bio-inspired computational intelligence, wireless sensor networks, and distributed computing.

Asad I. Khan is a senior lecturer in the Faculty of Information Technology at Monash University. Dr. Khan is an Australian Research Council assessor and has published over 80 refereed papers. His research areas include parallel computation, neural networks, and distributed pattern recognition as well as the development of e-research systems and intelligent sensor networks.

Benny Nasution is with the Department of Computer Engineering at Politeknik Negeri Medan. Dr. Nasution was awarded the IBM Award from Tokyo Research Lab and the Mollie Holman Medal from Monash University.