Center for Computing Research (CCR)
Center for Computing Research (CCR)

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Uncertainty Quantification for Machine Learning

Stracuzzi, David J.; Chen, Maximillian G.; Darling, Michael C.; Peterson, Matthew G.; Vollmer, Charlie

In this paper, we assert the importance of uncertainty quantification for machine learning and sketch an initial research agenda. We define uncertainty in the context of machine learning, identify its sources, and motivate the importance and impact of its quantification. We then illustrate these issues with an image analysis example. The paper concludes by identifying several specific research issues and by discussing the potential long-term implications of uncertainty quantification for data analytics in general.

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TYPE SAND Report YEAR 2017
DOIOSTI

Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

Statistical Analysis and Data Mining

Stracuzzi, David J.; Brost, Randolph; Phillips, Cynthia A.; Robinson, David G.; Wilson, Alyson G.; Woodbridge, Diane M.

Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. We present a preliminary evaluation of three methods for determining both match quality scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.

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TYPE Journal Article YEAR 2015
DOIOSTIScopus

Preliminary Results on Uncertainty Quantification for Pattern Analytics

Stracuzzi, David J.; Brost, Randolph; Chen, Maximillian G.; Malinas, Rebecca; Peterson, Matthew G.; Phillips, Cynthia A.; Robinson, David G.; Woodbridge, Diane M.

This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search, and discuss a number of possible improvements for each.

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TYPE SAND Report YEAR 2015
DOIOSTI
Results 26–42 of 42
Results 26–42 of 42
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