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Extreme Multi-Label Classification

5 papers with code · Methodology

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Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification

17 Apr 2019tomtung/omikuji

In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

19
17 Apr 2019

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

85
27 Oct 2018

Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification

12 Feb 2018hiro4bbh/sticker

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

4
12 Feb 2018

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

8 Sep 2016Refefer/fastxml

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

106
08 Sep 2016