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 selection of a validation basis from a full dataset is often required inindustrial use of supervised machine learning algorithm. This validation basiswill serve to realize an independent evaluation of the machine learning model.To select this basis, we propose to adopt a "design of experiments" point ofview, by using statistical criteria. We show that the "support points" concept,based on Maximum Mean Discrepancy criteria, is particularly relevant. Anindustrial test case from the company EDF illustrates the practical interest ofthe methodology.

Sample selection from a given dataset to validate machine learning models