Learning common and label-specific features for multi-Label classification with correlation information

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Related collections

Most cited references 33

Record: found
Abstract: not found
Article: not found

ML-KNN: A lazy learning approach to multi-label learning

Min-Ling Zhang, Zhi-Hua Zhou (2007)

0 comments Cited 380 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

A Review on Multi-Label Learning Algorithms

Min-Ling Zhang, Zhi-Hua Zhou (2014)

0 comments Cited 354 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Learning multi-label scene classification

Matthew R. Boutell, Jiebo Luo, Xipeng Shen … (2004)

0 comments Cited 252 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Title: Pattern Recognition

Abbreviated Title: Pattern Recognition

Publisher: Elsevier BV

ISSN (Print): 00313203

Publication date Created: January 2022

Publication date (Print): January 2022

Volume: 121

Page: 108259

Article

DOI: 10.1016/j.patcog.2021.108259

SO-VID: a69a39b4-e523-490d-9c25-1f75560c8b5e

License:

https://www.elsevier.com/tdm/userlicense/1.0/

https://doi.org/10.15223/policy-017

https://doi.org/10.15223/policy-037

https://doi.org/10.15223/policy-012

https://doi.org/10.15223/policy-029

https://doi.org/10.15223/policy-004

History

Data availability:

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.