Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University
Abstract:
Current machine learning architectures, strategies, and methods are typically static and non-interactive, making them incapable of adapting to changing and/or heterogeneous data environments, either in real-time, or in near-real-time. Typically, in real-time applications, large amounts of disparate data must be processed, learned from, and actionable
intelligence provided in terms of recognition of evolving activities….
Full access to this whitepaper requires submitting the form on the following page. Click the link below to continue.
[download id=”6912″]