PU Learning Tutorial
1. PU Learning and its sources
2. PU Learning definitions
3. Assumptions to enable PU Learning
4. Two-Step Techniques
5. Biased Learning
6. Incorporation of the labeling mechanism
3. Assumptions to enable PU Learning
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Slides
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Content
Labeling mechanism assumptions
Selected Completely At Random (SCAR)
Selected At Random (SAR)
Probabilistic Gap
Data (class distribution) assumptions
Negativity
Separability
Smoothness
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2. PU Learning definitions
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4. Two-Step Techniques