Internet-Scale Pattern Recognition

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: N

This book gives a comprehensive and systematic review of secure compressive sensing (CS) for applications in various fields such as image processing, pattern recognition, Internet of things (IoT), and

Businesses are rushing to collect personal data to fuel surging demand. Data enthusiasts claim personal information that's obtained from the commercial internet, including mobile platforms, social networks, cloud computing, and connected devices, will unlock path-breaking innovation, including advanced data security. By contrast, regulators and activists contend that corporate data practices too often disempower consumers by creating privacy harms and related problems. As the Internet of Things matures and facial recognition, predictive analytics, big data, and wearable tracking grow in power, scale, and scope, a controversial ecosystem will exacerbate the acrimony over commercial data capture and analysis. The only productive way forward is to get a grip on the key problems right now and change the conversation. That's exactly what Jules Polonetsky, Omer Tene, and Evan Selinger do. They bring together diverse views from leading academics, business leaders, and policymakers to discuss

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and com

Data Management and Internet Computing for Image/Pattern Analysis focuses on the data management issues and Internet computing aspect of image processing and pattern recognition research. The book