Ensemble Learning With Imbalanced Data
We describe an ensemble approach to learning salient spatial regions from arbitrarily partitioned simulation data. Ensemble approaches for anomaly detection are also explored. The partitioning comes from the distributed processing requirements of large-scale simulations. The volume of the data is su...
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| Format: | Others |
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Scholar Commons
2010
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| Online Access: | http://scholarcommons.usf.edu/etd/3589 http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=4860&context=etd |