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Technical Report.pdf

lou_14_structured.pdf
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🔗 Source: hci.iwr.uni-heidelberg.de
📊 Size: 8.35 MB
📄 Pages: 27 pages
⬇️ Downloads: 77

Summary

Structured learning has broadened machine learning applications, but it requires heavy ground truth annotation, which is expensive and challenging, especially for complex, structured data. This chapter addresses this issue by enabling structured learning with "cheap data" that requires less annotation. Three methods are considered: structured learning from partial annotations, active learning, and transfer learning. These methods are demonstrated on a cell tracking problem, showing a substantial reduction in annotation effort while maintaining model quality. Structured output predictions can be represented as graphs with vertex and edge attributes, and supervised structured learning optimizes parameters to reproduce expert annotations. However, generating expansive training data is tedious, and this chapter explores learning approaches that can make do with less human effort.

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Description

Structured learning has broadened machine learning applications, but it requires heavy ground truth annotation, which is expensive and challenging, especially...

Technical Information

  • File Format: PDF
  • File Size: 8.35 MB
  • Pages: 27
  • Language: EN
  • Total Downloads: 77
  • Last Updated: 2 hours ago

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