Internet-Scale Pattern Recognition

New Techniques for Voluminous Data Sets and Data Clouds

By Anang Hudaya Muhamad Amin, Asad I. Khan, Benny B. Nasution

© 2013 – Chapman and Hall/CRC

197 pages | 76 B/W Illus.

Purchasing Options:
Hardback: 9781466510968
pub: 2012-11-19
US Dollars$109.95

About the Book

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


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


About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Database Management / Data Mining
COMPUTERS / Machine Theory
COMPUTERS / Internet / General