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 predictive ability of database classifiers constructed with a network ofinteracting chemical oscillators is studied. Databases considered here arecomposed of records, where each record contains a number of parameters(predictors) characterizing the case and the output variable describing thecase type. In a series of recent papers we have discussed the top-down designof database classifiers, that return output information as the number ofexcitations observed at a selected droplet of the network. The design is basedon an evolutionary algorithm that optimizes the network to achieve maximummutual information between the network evolution and the record types. Here wediscuss results illustrating that such classifiers do have a predictingability. They are able to give a correct classification for database recordsthat are not included into the process of classifier training. As exampledatabases we consider points inside a multidimensional unit cube and test ifthey belong to a ball located at the mid of cube.

On the predictive power of database classifiers formed by a small network of interacting chemical oscillators