English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multi-label learning has attracted the attention of the machine learningcommunity. The problem conversion method Binary Relevance converts a familiarsingle label into a multi-label algorithm. The binary relevance method iswidely used because of its simple structure and efficient algorithm. But binaryrelevance does not consider the links between labels, making it cumbersome tohandle some tasks. This paper proposes a multi-label learning algorithm thatcan also be used for single-label classification. It is based on standardsupport vector machines and changes the original single decision hyperplaneinto two parallel decision hyper-planes, which call multi-label parallelsupport vector machine (MLPSVM). At the end of the article, MLPSVM is comparedwith other multi-label learning algorithms. The experimental results show thatthe algorithm performs well on data sets.

MLPSVM:A new parallel support vector machine to multi-label learning