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

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

November 20, 2012 by Chapman and Hall/CRC
Reference - 197 Pages - 76 B/W Illustrations
ISBN 9781466510968 - CAT# K14810


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  • 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


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.