Internet-Scale Pattern Recognition

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

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 that address 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, processing, distributed systems, and data . 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 of large networks of devices.

Table of Contents

Part I: Recognition: A New Perspective
Chapter 1: Introduction
Chapter 2: Distributed Approach for Pattern Recognition
Part II: Evolution of Internet-Scale Recognition
Chapter 3: One-Shot Learning Considerations
Chapter 4: Hierarchical Model for Pattern Recognition
Chapter 5: Recognition via Divide-and-Distribute Approach
Part III: Systems and Tools
Chapter 6: Internet-Scale Applications Development
Part IV: Implementations and Applications
Chapter 7: Multi-Feature Classifications for Complex Data
Chapter 8: Pattern Recognition within Coarse-Grained Networks
Chapter 9: Detection within Fine-Grained Networks
Part V: The Way Forward
Chapter 10: Recognition: The Future and Beyond

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