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.
I Recognition: A New Perspective: Introduction. Distributed Approach for Pattern Recognition. II Evolution of Internet-Scale Recognition: One-Shot Learning Considerations. Hierarchical Model for Pattern Recognition. Recognition via a Divide-and-Distribute Approach. III Systems and Tools: Internet-Scale Applications Development. IV Implementations and Applications: Multi-Feature Classifications for Complex Data. Pattern Recognition within Coarse-Grained Networks. Event Detection within Fine-Grained Networks. Recognition: The Future and Beyond. Bibliography.