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 simple, yet effective, approach to Semi-Supervised Learning. Ourapproach is based on estimating density-based distances (DBD) using a shortestpath calculation on a graph. These Graph-DBD estimates can then be used in anydistance-based supervised learning method, such as Nearest Neighbor methods andSVMs with RBF kernels. In order to apply the method to very large data sets, wealso present a novel algorithm which integrates nearest neighbor computationsinto the shortest path search and can find exact shortest paths even inextremely large dense graphs. Significant runtime improvement over the commonlyused Laplacian regularization method is then shown on a large scale dataset.

Semi-supervised Learning with Density Based Distances