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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this work anexample-basedoutlier detection method exploiting both positive (that is, outlier) and negative (that is, inlier) examples in order to guide the search for anomalies in an unlabelled data set, is introduced. The key idea of the method is to find the subspace where positive examples mostly exhibit their outlierness while at the same time negative examples mostly exhibit their inlierness. The degree to which an example is an outlier is measured by means of well-known unsupervised outlier scores evaluated on the collection of

FINDING DISTANCE-BASED OUTLIERS IN SUBSPACES THROUGH BOTH POSITIVE AND NEGATIVE EXAMPLES