Research Challenges in Extreme Multi-label Classification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0769-4_56
Reference33 articles.
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3. Prabhu Y, Kag A, Harsola S, Agrawal R, Varma M (2018) Parabel: partitioned label trees for extreme classification with application to dynamic search advertising. In: Proceedings of the 2018 world wide web conference, pp 993–1002
4. Prabhu Y, Varma M (2014) Fastxml: a fast, accurate and stable tree-classifier for extreme multi-label learning. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 263–272
5. Xu C, Tao D, Xu C (2016) Robust extreme multi-label learning. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1275–1284
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