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McCallum And Nigam.pdf

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Preview of McCallum and Nigam
🔗 Source: kamalnigam.com
📊 Size: 369 KB
📄 Pages: 9 pages
⬇️ Downloads: 468

Summary

The authors modify the Query-by-Committee (QBC) active learning method to select examples for labeling from the entire pool of unlabeled documents and estimate document density. They also combine active learning with Expectation-Maximization (EM) to "fill in" the class labels of remaining unlabeled documents. Experimental results show that the improved active learning requires less than two-thirds as many labeled training examples as previous QBC approaches, and the combination of EM and active learning requires only slightly more than half as many labeled training examples to achieve the same accuracy as either method alone.

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Description

The authors modify the Query-by-Committee (QBC) active learning method to select examples for labeling from the entire pool of unlabeled documents and estimate...

Technical Information

  • File Format: PDF
  • File Size: 369 KB
  • Pages: 9
  • Language: EN
  • Total Downloads: 468
  • Last Updated: 9 hours ago

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