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

In this work, we develop a simple algorithm for semi-supervised regression.The key idea is to use the top eigenfunctions of integral operator derived fromboth labeled and unlabeled examples as the basis functions and learn theprediction function by a simple linear regression. We show that underappropriate assumptions about the integral operator, this approach is able toachieve an improved regression error bound better than existing bounds ofsupervised learning. We also verify the effectiveness of the proposed algorithmby an empirical study.

A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound