Chained Deep Learning Using Generalized Cross-Entropy for Multiple Annotators Classification

Dynamic graph structure learning for multivariate time series forecasting

(2023) Zhuo Lin Li et al.   PATTERN RECOGNITION

Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective

(2022) Jing Zhang   IEEE-CAA Journal of Automatica Sinica

Learning From Noisy Labels With Deep Neural Networks: A Survey

(2022) Hwanjun Song et al.   IEEE Transactions on Neural Networks and Learning Systems

Regularized Chained Deep Neural Network Classifier for Multiple Annotators

(2021) Julián Gil-González et al.   Applied Sciences-Basel

L1-norm constraint kernel adaptive filtering framework for precise and robust indoor localization under the internet of things

(2021) Xin Zhao et al.   INFORMATION SCIENCES

Correlated Chained Gaussian Processes for Datasets With Multiple Annotators

(2021) J. Gil-González et al.   IEEE Transactions on Neural Networks and Learning Systems

Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM

(2020) José Alberto Hernández-Muriel et al.   Applied Sciences-Basel

Learning from multiple inconsistent and dependent annotators to support classification tasks

(2020) J. Gil-Gonzalez et al.   NEUROCOMPUTING

Scalable and Efficient Learning from Crowds with Gaussian Processes

(2019) Pablo Morales-Álvarez et al.   Information Fusion

Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices

(2019) Louise Leroux et al.   EUROPEAN JOURNAL OF AGRONOMY

Crowdsourced Label Aggregation Using Bilayer Collaborative Clustering

(2019) Jing Zhang et al.   IEEE Transactions on Neural Networks and Learning Systems

Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalising Care

(2018) Tingting Zhu et al.   IEEE Journal of Biomedical and Health Informatics

Learning from multiple annotators using kernel alignment

(2018) J. Gil-Gonzalez et al.   PATTERN RECOGNITION LETTERS

Learning from crowds with variational Gaussian processes

(2018) Pablo Ruiz et al.   PATTERN RECOGNITION

Bi-convex Optimization to Learn Classifiers from Multiple Biomedical Annotations

(2017) Xin Wang et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

(2016) Shadi Albarqouni et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Imbalanced Multiple Noisy Labeling

(2015) Jing Zhang et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Making risk minimization tolerant to label noise

(2015) Aritra Ghosh et al.   NEUROCOMPUTING

Modeling annotator behaviors for crowd labeling

(2015) Yunus Emre Kara et al.   NEUROCOMPUTING

Learning from multiple annotators with varying expertise

(2013) Yan Yan et al.   MACHINE LEARNING

Sequence labeling with multiple annotators

(2013) Filipe Rodrigues et al.   MACHINE LEARNING

Learning from multiple annotators: Distinguishing good from random labelers

(2013) Filipe Rodrigues et al.   PATTERN RECOGNITION LETTERS