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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Regular data classification techniques are based mainly on two strong assumptions: (1) the existence of a reasonably large labeled set of data to be used in training; and (2) future input data instances conform to the distribution of the training set, i.e. data distribution is stationary along time. However, in the case of data stream classification, both of the aforementioned assumptions are difficult to satisfy. In this paper, we present a graph-based semi-supervised approach that extends the static classifier based on the K-associated Optimal Graph to perform online semi-supervised classification tasks. In order to learn from labeled and unlabeled patterns, here we adapt the optimal graph construction to simultaneously spread the labels in the training set. The sparse, disconnected nature of the proposed graph structure gives flexibility to cope with non-stationary classification. Experimental comparison between the proposed method and three state-of-the-art ensemble classification methods is provided and promising results have been obtained.

Partially labeled data stream classification with the semi-supervised K-associated graph