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13 April 2009 Autonomous journaling response using data model LUTS
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Abstract
Matching journal entries to appropriate context responses can be a daunting problem, especially when there are no salient keyword matches between the entry and the proposed library of appropriate responses. We examine a real-world application for matching interactive journaling requests for guidance to an a priori established archive of sufficient multimedia responses. We show the analysis required to enable a Data Model based algorithm to group journaling entries according to intrinsic context information and type. We demonstrate a new lookup table (LUT) classifier that exploits all available data in LUT form.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Holger Jaenisch, James Handley, Nathaniel Albritton, David Whitener, Randel Burnett, Robert Caspers, Stephen Moren, Thomas Alexander, William Maddox III, and William Albritton Jr. "Autonomous journaling response using data model LUTS", Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 734409 (13 April 2009); https://doi.org/10.1117/12.817863
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