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Enabling Efficiency-Precision Trade-offs for Label Trees in..:
, In:
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
,
Baharav, Tavor Z.
;
Jiang, Daniel L.
;
Kolluri, Kedarnath
.. - p. 3717-3726 , 2021
Link:
https://dl.acm.org/doi/10.1145/3459637.3481914
RT T1 <cTitle>Proceedings of the 30th ACM International Conference on Information & Knowledge Management</cTitle>: T1 <Title>Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3481914&Exemplar=1&LAN=DE A1 Baharav, Tavor Z. A1 Jiang, Daniel L. A1 Kolluri, Kedarnath A1 Sanghavi, Sujay A1 Dhillon, Inderjit S. PB ACM YR 2021 K1 extreme multi-label classification K1 probabilistic label trees K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Classification and regression trees K1 Mathematics of computing K1 Information theory K1 Coding theory K1 Information systems K1 Information retrieval K1 Retrieval tasks and goals K1 Recommender systems SP 3717 OP 3726 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3459637.3481914 DO https://dl.acm.org/doi/10.1145/3459637.3481914 SF ELIB - SuUB Bremen
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