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 consider a new low-quality label learning problem: learningtime series detection models from temporally imprecise labels. In this problem,the data consist of a set of input time series, and supervision is provided bya sequence of noisy time stamps corresponding to the occurrence of positiveclass events. Such temporally imprecise labels commonly occur in areas likemobile health research where human annotators are tasked with labeling theoccurrence of very short duration events. We propose a general learningframework for this problem that can accommodate different base classifiers andnoise models. We present results on real mobile health data showing that theproposed framework significantly outperforms a number of alternatives includingassuming that the label time stamps are noise-free, transforming the probleminto the multiple instance learning framework, and learning on labels that weremanually re-aligned.

Learning Time Series Detection Models from Temporally Imprecise Labels