Extreme Multi-Label Classification
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DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING
A no-regret generalization of hierarchical softmax to extreme multi-label classification
NeurIPS 2018
• mwydmuch/extremeText
• 
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
EMNLP 2018 • ShimShim46/HFT-CNN
The lower the HS level, the less the categorization performance.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION TEXT CATEGORIZATION
Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification
Finally, we show that the Sparse Weighted Nearest-Neighbor Method can process data points in real time on XMLC datasets with equivalent performance to SOTA models, with a single thread and smaller storage footprint.
EXTREME MULTI-LABEL CLASSIFICATION INFORMATION RETRIEVAL MULTI-LABEL CLASSIFICATION
Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification
Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousand or even millions of labels.
EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING