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Learning Classifiers from Distributed Data Sources
Abstract
Recent development of high throughput data acquisition technologies in a number of domains (e.g., biological sciences, atmospheric sciences, space sciences, commerce) together with advances in digital storage, computing, and communications technologies have resulted in the proliferation of a multitude of physically distributed data repositories created and maintained by autonomous entities (e.g., scientists, organizations). The resulting increasingly data-rich domains offer unprecedented opportunities in computer assisted data-driven knowledge acquisition in a number of applications, including, in particular, data-driven scientific discovery, data-driven decision-making in business and commerce, monitoring and control of complex systems, and security informatics.
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