Improving Multi-label Classification by Avoiding Implicit Negativity with Incomplete Data
Many real world problems require multi-label classification, in which each training instance is associated with a set of labels. There are many existing learning algorithms for multi-label classification; however, these algorithms assume implicit negativity, where missing labels in the training data...
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| Format: | Others |
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BYU ScholarsArchive
2011
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| Online Access: | https://scholarsarchive.byu.edu/etd/2844 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3843&context=etd |