Problem Definition

Kristen Jaskie; Andreas Spanias

doi:10.1007/978-3-031-79178-9_2

Problem Definition

Kristen Jaskie, Andreas Spanias

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

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
Publisher	Springer Nature
Pages	13-34
Number of pages	22
DOIs	https://doi.org/10.1007/978-3-031-79178-9_2
State	Published - 2022

Publication series

Name	Synthesis Lectures on Artificial Intelligence and Machine Learning
ISSN (Print)	1939-4608
ISSN (Electronic)	1939-4616

ASJC Scopus subject areas

Artificial Intelligence

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10.1007/978-3-031-79178-9_2

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Problem Definition

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