9 citations · 33 across the 10 of their papers we have counts for
Showing 2013 · cs.LGShow all
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cs.LG2013
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
Michael R. Smith, Tony Martinez
Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. Severa…
cs.LG2013★ 9 cited
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks
Michael R. Smith, Tony Martinez
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling o…