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Extreme Multi-Label Classification

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.

Papers

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Showing 61–70 of 75 papers

TitleStatusHype
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
Learning from eXtreme Bandit Feedback—0
Learning label-label correlations in Extreme Multi-label Classification via Label Features—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Locally Non-linear Embeddings for Extreme Multi-label Learning—0
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