In this chapter, we present the Positive and Unlabeled (PU) learning problem in detail. In addition to notation and a formal statement of the problem, we explore fundamental factors that influence PU models such as how the source data for the model was acquired and what assumptions are made about that data. We end the chapter with a brief description of two closely related learning problems, one class classification (OCC) and noisy learning.
|Original language||English (US)|
|Title of host publication||Synthesis Lectures on Artificial Intelligence and Machine Learning|
|Number of pages||22|
|State||Published - 2022|
|Name||Synthesis Lectures on Artificial Intelligence and Machine Learning|
ASJC Scopus subject areas
- Artificial Intelligence