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

We present a clustering- and demotion-based algorithm called Kmeans-FOLD toinduce nonmonotonic logic programs from positive and negative examples. Ouralgorithm improves upon-and is inspired by-the FOLD algorithm. The FOLDalgorithm itself is an improvement over the FOIL algorithm. Our algorithmgenerates a more concise logic program compared to the FOLD algorithm. Ouralgorithm uses the K-means based clustering method to cluster the inputpositive samples before applying the FOLD algorithm. Positive examples that arecovered by the partially learned program in intermediate steps are notdiscarded as in the FOLD algorithm, rather they are demoted, i.e., theirweights are reduced in subsequent iterations of the algorithm. Our experimentson the UCI dataset show that a combination of K-Means clustering and ourdemotion strategy produces significant improvement for datasets with more thanone cluster of positive examples. The resulting induced program is also moreconcise and therefore easier to understand compared to the FOLD and ALEPHsystems, two state of the art inductive logic programming (ILP) systems.

A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories