GAME: GAussian Mixture Error-based meta-learning architecture
In supervised learning, the gap between the truth label and the model output is always
portrayed by an error function, and a fixed error function corresponds to a specific noise …
portrayed by an error function, and a fixed error function corresponds to a specific noise …
[HTML][HTML] Intelligent ironmaking optimization service on a cloud computing platform by digital twin
The shortage of computation methods and storage devices has largely limited the
development of multi-objective optimization in industrial processes. To improve the …
development of multi-objective optimization in industrial processes. To improve the …
Investigating annotation noise for named entity recognition
Recent studies revealed that even the most widely used benchmark dataset still contains
more than 5% sample-level annotation noise in Named Entity Recognition (NER). Hence …
more than 5% sample-level annotation noise in Named Entity Recognition (NER). Hence …
Investigating Machine Learning Techniques Used for the Detection of Class Noise in Data: A Systematic Literature Review
C van den Berg, S Eybers - Science and Information Conference, 2024 - Springer
Data provides valuable information and insights and assists in making strategic decisions.
The quality of the data is distorted by noise, which negatively affects information, insights …
The quality of the data is distorted by noise, which negatively affects information, insights …
Robust Noisy Label Learning via Two-Stream Sample Distillation
S Bai, S Zhou, Z Qin, L Wang, N Zheng - arXiv preprint arXiv:2404.10499, 2024 - arxiv.org
Noisy label learning aims to learn robust networks under the supervision of noisy labels,
which plays a critical role in deep learning. Existing work either conducts sample selection …
which plays a critical role in deep learning. Existing work either conducts sample selection …
JSMix: a holistic algorithm for learning with label noise
The success of deep learning is mainly dependent on large-scale and accurately labeled
datasets. However, real-world datasets are marked with much noise. Directly training on …
datasets. However, real-world datasets are marked with much noise. Directly training on …
Research on a Pattern Recognition Method of Cyclic GMM-FCM Based on Joint Time-Domain Features
Y Li, Z Wang, T Zhao, S Wanqing - Ieee Access, 2020 - ieeexplore.ieee.org
The safety and reliability of the mechanical system in the industrial process determines the
quality of products. Whether the fault can be identified and classified in time is the key to …
quality of products. Whether the fault can be identified and classified in time is the key to …
Check for updates Investigating Machine Learning Techniques Used for the Detection of Class Noise in Data: A Systematic Literature Review
C van den Berg¹, S Eybers - … of the 2024 Computing Conference, Volume … - books.google.com
Data provides valuable information and insights and assists in making strategic decisions.
The quality of the data is distorted by noise, which negatively affects information, insights …
The quality of the data is distorted by noise, which negatively affects information, insights …