Cargando…
Two-step learning for crowdsourcing data classification
Crowdsourcing learning (Bonald and Combes 2016; Dawid and Skene, J R Stat Soc: Series C (Appl Stat), 28(1):20–28 1979; Karger et al. 2011; Li et al, IEEE Trans Knowl Data Eng, 28(9):2296–2319 2016; Liu et al. 2012; Schlagwein and Bjorn-Andersen, J Assoc Inform Syst, 15(11):3 2014; Zhang et al. 2014)...
Autores principales: | Yu, Hao, Li, Jiaye, Wu, Zhaojiang, Xu, Hang, Zhu, Lei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510273/ https://www.ncbi.nlm.nih.gov/pubmed/36188185 http://dx.doi.org/10.1007/s11042-022-12793-4 |
Ejemplares similares
-
Measuring Urban Vibrancy of Residential Communities Using Big Crowdsourced Geotagged Data
por: Wang, Pengyang, et al.
Publicado: (2021) -
A novel self-learning semi-supervised deep learning network to detect fake news on social media
por: Li, Xin, et al.
Publicado: (2021) -
Fair classification via domain adaptation: A dual adversarial learning approach
por: Liang, Yueqing, et al.
Publicado: (2023) -
Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks
por: Abu Al-Haija, Qasem
Publicado: (2022) -
Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
por: Rath, Adyasha, et al.
Publicado: (2022)