Semantic Scholar Open Access 2018 669 sitasi

Learning from positive and unlabeled data: a survey

Jessa Bekker Jesse Davis

Lihat Sumber DOI

Abstrak

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (2)

Jessa Bekker

Jesse Davis

Format Sitasi

APA MLA BibTeX

Bekker, J., Davis, J. (2018). Learning from positive and unlabeled data: a survey. https://doi.org/10.1007/s10994-020-05877-5

Akses Cepat

Lihat di Sumber doi.org/10.1007/s10994-020-05877-5

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 669×
Sumber Database: Semantic Scholar
DOI: 10.1007/s10994-020-05877-5
Akses: Open Access ✓