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 many real-world machine learning applications, unlabeled samples are easyto obtain, but it is expensive and/or time-consuming to label them. Activelearning is a common approach for reducing this data labeling effort. Itoptimally selects the best few samples to label, so that a better machinelearning model can be trained from the same number of labeled samples. Thispaper considers active learning for regression (ALR) problems. Three essentialcriteria -- informativeness, representativeness, and diversity -- have beenproposed for ALR. However, very few approaches in the literature haveconsidered all three of them simultaneously. We propose three new ALRapproaches, with different strategies for integrating the three criteria.Extensive experiments on 12 datasets in various domains demonstrated theireffectiveness.

Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression