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Proceedings Paper

Distributed classifier chain optimization for real-time multimedia stream mining systems
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Paper Abstract

We consider the problem of optimally configuring classifier chains for real-time multimedia stream mining systems. Jointly maximizing the performance over several classifiers under minimal end-to-end processing delay is a difficult task due to the distributed nature of analytics (e.g. utilized models or stored data sets), where changing the filtering process at a single classifier can have an unpredictable effect on both the feature values of data arriving at classifiers further downstream, as well as the end-to-end processing delay. While the utility function can not be accurately modeled, in this paper we propose a randomized distributed algorithm that guarantees almost sure convergence to the optimal solution. We also provide results using speech data showing that the algorithm can perform well under highly dynamic environments.

Paper Details

Date Published: 28 January 2008
PDF: 9 pages
Proc. SPIE 6820, Multimedia Content Access: Algorithms and Systems II, 68200N (28 January 2008); doi: 10.1117/12.766549
Show Author Affiliations
Brian Foo, Univ. of California, Los Angeles (United States)
Mihaela van der Schaar, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 6820:
Multimedia Content Access: Algorithms and Systems II
Theo Gevers; Ramesh C. Jain; Simone Santini, Editor(s)

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