A key problem in multi-label classification is to utilize dependencies among the labels. Chaining classifiers are a simple technique for addressing.
In this work, we propose a multi-label classification approach which allows to choose a dynamic, context-dependent label ordering. Our proposed approach ...
Learning Context-dependent Label Permutations for Multi-label Classification ... We need MLC algorithms that learn context-dependent label permutations.
This work pro-poses a multi-label classification approach which allows to choose a dynamic, context-dependent label ordering, and obtains a powerful ...
Duration: 23:30
Posted: Jul 27, 2019
Posted: Jul 27, 2019
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Our new method, Adaptive KNowledge Transfer (ANT), trains a student by learning from each teacher's partial knowledge of label dependencies to infer the global ...
... Label Dependence and Loss Minimization in Multi-label Classification”, 2012. Page 18. 13. Label Dependence and Loss Metrics. Suppose conditional dependence,. P ...
Jun 15, 2019 · It combines the computational efficiency of Binary Relevance method and takes the label dependency into account for classification tasks.
Jul 24, 2024 · In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels ...
Feb 7, 2023 · Multi-label classification (MLC), which assigns multiple labels to each instance, is crucial to domains from computer vision to text mining.