Label compression (LC) is an effective strategy to reduce time cost and improve classification performance simultaneously for multi-label classification. One ...
Label compression (LC) is an effective strategy to reduce time cost and improve classification performance simultaneously for multi-label classification.
Label compression (LC) is an effective strategy to reduce time cost and improve classification performance simultaneously for multi-label classification. One ...
Label compression (LC) is an effective strategy to reduce time cost and improve classification performance simultaneously for multi-label classification.
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The Hill loss [11] is a loss function designed for robust multi-label classification with missing labels. The loss is defined as a weighted mean-squared ...
Jujie Zhang, Min Fang, Jin-Qiao Wu, Xiao Li: Robust label compression for multi-label classification. Knowl. Based Syst. 107: 32-42 (2016).
Dec 12, 2023 · Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, ...
Missing: compression | Show results with:compression
In this paper, we present a Bayesian framework for multilabel classification using compressed sensing. The key idea in compressed sensing for multilabel ...
In multi-label classification tasks, labels are com- monly related with each other. It has been well rec- ognized that utilizing label relationship is ...
The label compression improves both the effectiveness and efficiency, and the coherent optimization mutually benefits the label matrix and predictor. Published ...
Missing: classification. | Show results with:classification.