Overview

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.

Full Product Details

Author:   Anang Hudaya Muhamad Amin ,  Asad I. Khan ,  Benny B. Nasution
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
ISBN:  

9781466510975

ISBN 10:   1466510978
Pages:   197
Publication Date:   20 November 2012
Audience:   General/trade ,  College/higher education ,  General ,  Tertiary & Higher Education
Format:   Electronic book text
Publisher's Status:   Active
Availability:   Available To Order  
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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

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Author Information

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.

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