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

Kernel method-based one-class classifier is mainly used for outlier ornovelty detection. In this letter, kernel ridge regression (KRR) basedone-class classifier (KOC) has been extended for learning using privilegedinformation (LUPI). LUPI-based KOC method is referred to as KOC+. Thisprivileged information is available as a feature with the dataset but only fortraining (not for testing). KOC+ utilizes the privileged informationdifferently compared to normal feature information by using a so-calledcorrection function. Privileged information helps KOC+ in achieving bettergeneralization performance which is exhibited in this letter by testing theclassifiers with and without privileged information. Existing and proposedclassifiers are evaluated on the datasets from UCI machine learning repositoryand also on MNIST dataset. Moreover, experimental results evince the advantageof KOC+ over KOC and support vector machine (SVM) based one-class classifiers.

OCKELM+: Kernel Extreme Learning Machine based One-class Classification  using Privileged Information (or KOC+: Kernel Ridge Regression or Least  Square SVM with zero bias based One-class Classification using Privileged  Information)