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Trusted-Data-Guided Label Enhancement on Noisy Labels
IEEE Transactions on Neural Networks and Learning Systems, 2023Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. Label enhancement (LE) is a procedure of recovering the label distribution from the logical labels in the training data, the purpose of which is to better depict the label ambiguity through label distribution.
Ning Xu +3 more
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AI Assisted Data Labeling with Interactive Auto Label
Proceedings of the AAAI Conference on Artificial Intelligence, 2022We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised machine learning to facilitate accurate and efficient labeling of large data sets. Our system (1) applies representative data sampling and active learning in order to seed and maintain a semi-supervised learner that assists the human labeler (2) provides ...
Michael Desmond +6 more
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Analysis of dynamic labeling data
Mathematical Biosciences, 2004Comprehensive assessments of the organization and regulation of metabolic pathways cannot be limited to steady-state measurements alone but require dynamic time series data. One experimental means of generating such data consists of radioactively labeling precursors and measuring their fate over time.
Voit, Eberhard O. +2 more
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Don’t Start Your Data Labeling from Scratch: OpSaLa - Optimized Data Sampling Before Labeling
2023Many text classification tasks face a severe class imbalance problem that limits the ability to train high-performance models. This is partly due to the small number of instances in the minority class, so that the minority class patterns are not well-represented.
Pelicon, Andraž +2 more
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Assessing the Multi-labelness of Multi-label Data
2020Before constructing a classifier, we should examine the data to gain an understanding of the relationships between the variables, to assist with the design of the classifier. Using multi-label data requires us to examine the association between labels: its multi-labelness.
Laurence A. F. Park, Yi Guo, Jesse Read
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Lymphocyte kinetics: the interpretation of labelling data
Trends in Immunology, 2002DNA labelling provides an exciting tool for elucidating the in vivo dynamics of lymphocytes. However, the kinetics of label incorporation and loss are complex and results can depend on the method of interpretation. Here we describe two approaches to interpreting labelling data.
Asquith, B. +4 more
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1974
(Uploaded by Plazi from the Biodiversity Heritage Library) No abstract provided.
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(Uploaded by Plazi from the Biodiversity Heritage Library) No abstract provided.
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2009
A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.
Grigorios Tsoumakas +2 more
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A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.
Grigorios Tsoumakas +2 more
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Candidate Label-aware Similarity Graph for Partial Label Data
2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019In partial label learning, the label of instance is represented by a label set, among which only one is the true label. The existing partial label learning algorithm only use features of instances to measure the similarity, and lacks of utilization of information hidden in the label set.
Tian Xie +4 more
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Learning from Partially Labeled Data
2020Providing sufficient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications.
Mehrkanoon, Siamak +2 more
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