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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

Showing 4150 of 75 papers

TitleStatusHype
Extreme Multi-label Learning for Semantic Matching in Product Search0
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification0
Priberam at MESINESP Multi-label Classification of Medical Texts TaskCode0
Fine-grained Generalization Analysis of Vector-valued Learning0
Stratified Sampling for Extreme Multi-Label DataCode0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Multi-label Ranking: Mining Multi-label and Label Ranking Data0
Tensor Composition Net for Visual Relationship Prediction0
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification FrameworkCode1
The Emerging Trends of Multi-Label Learning0
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