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

A number of results related to statistical classification on convex sets arepresented. In particular, the focus is on the case where some of the covariatesin the data and observation being classified can be missing. The form of theoptimal classifier is derived when the class-conditional densities are uniformover convex regions. In practice, the underlying convex sets are often unknownand must be estimated with a set of data. In this case, the convex hull of aset of points is shown to be a consistent estimator of the underlying convexset. The problem of estimation is further complicated since the number ofpoints in each convex hull is itself a random variable. The correspondingplug-in version of the optimal classifier is derived and shown to be Bayesconsistent.

Classification on convex sets in the presence of missing covariates