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

The standard approach to supervised classification involves the minimizationof a log-loss as an upper bound to the classification error. While this is atight bound early on in the optimization, it overemphasizes the influence ofincorrectly classified examples far from the decision boundary. Updating theupper bound during the optimization leads to improved classification rateswhile transforming the learning into a sequence of minimization problems. Inaddition, in the context where the classifier is part of a larger system, thismodification makes it possible to link the performance of the classifier tothat of the whole system, allowing the seamless introduction of externalconstraints.

Tighter bounds lead to improved classifiers