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 paper, we study learning in probabilistic domains where the learnermay receive incorrect labels but can improve the reliability of labels byrepeatedly sampling them. In such a setting, one faces the problem of whetherthe fixed budget for obtaining training examples should rather be used forobtaining all different examples or for improving the label quality of asmaller number of examples by re-sampling their labels. We motivate thisproblem in an application to compare the strength of poker hands where thetraining signal depends on the hidden community cards, and then study it indepth in an artificial setting where we insert controlled noise levels into theMNIST database. Our results show that with increasing levels of noise,resampling previous examples becomes increasingly more important than obtainingnew examples, as classifier performance deteriorates when the number ofincorrect labels is too high. In addition, we propose two different validationstrategies; switching from lower to higher validations over the course oftraining and using chi-square statistics to approximate the confidence inobtained labels.

Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability