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Abstract: Translating recent advances in high throughput data acquisition and data storage technologies into fundamental gains in understanding of respective domains calls for the design of learning algorithms (with provable performance guarantees) for knowledge acquisition from multiple distributed, dynamic data sources. We propose a theoretical framework for specification and analysis of a class of learning problems that arise in such environments. We introduce a family of learning operators for... (Update)
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BibTeX entry: (Update)
@misc{ agents-toward,
author = "Synthesis Of Agents",
title = "Toward a Theoretical Framework for Analysis and",
url = "citeseer.ist.psu.edu/730626.html" }
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