The SI-Combiner: Detecting and using scenario information for smart systems

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Many problems, such Aroclor Interpretation, are ill-conditioned problems in which trained programs, or methods, must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. Importantly, when multiple trained methods fail divergently, their patterns of failures provide insights into the true results. The SI-Combiner solves this problem of Integrating Multiple Learned Models (IMLM) by automatically learning and using these insights to produce a solution more accurate than any single trained program. In application, the Aroclor Interpretation SI-Combiner improved on the accuracy of the most accurate individual ... continued below

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10 p.

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Den Hartog, B.K.; Elling, J.W. & Kieckhafer, R.M. March 1, 1999.

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Many problems, such Aroclor Interpretation, are ill-conditioned problems in which trained programs, or methods, must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. Importantly, when multiple trained methods fail divergently, their patterns of failures provide insights into the true results. The SI-Combiner solves this problem of Integrating Multiple Learned Models (IMLM) by automatically learning and using these insights to produce a solution more accurate than any single trained program. In application, the Aroclor Interpretation SI-Combiner improved on the accuracy of the most accurate individual trained program in the suite. For Smart Systems, the implication of trusting a single computation or sensor that is not able to report its own accuracy is devastating. Once one begins to compare multiple results, majority rule may not make sense, especially for computations that are not provably independent. With approximate understanding of the conditions that confound an individual computation or sensor, the presented IMLM method allows sensible interpretation of multiple, possibly disagreeing, results. This paper presents a new fuzzy IMLM method called the SI-Combiner and its application to Aroclor Interpretation. Additionally, this paper shows the improvement in accuracy that the SI-Combiner`s components show against Multicategory Classification (MCC), Dempster-Shafer (DS), and the best individual trained program in the Aroclor Interpretation suite (iMLR).

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10 p.

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OSTI as DE99002036

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  • 8. topical on robotics and remote systems, Pittsburgh, PA (United States), 25-29 Apr 1999

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  • Other: DE99002036
  • Report No.: LA-UR--99-127
  • Report No.: CONF-990402--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 325785
  • Archival Resource Key: ark:/67531/metadc679001

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • March 1, 1999

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • June 14, 2016, 7:39 p.m.

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Den Hartog, B.K.; Elling, J.W. & Kieckhafer, R.M. The SI-Combiner: Detecting and using scenario information for smart systems, article, March 1, 1999; New Mexico. (digital.library.unt.edu/ark:/67531/metadc679001/: accessed April 24, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.