Match: a maximum-likelihood approach for classification under label shift (2025)
- Authors:
- Autor USP: SILVA, DIEGO FURTADO - ICMC
- Unidade: ICMC
- DOI: 10.1145/3711896.3737040
- Subjects: APRENDIZADO COMPUTACIONAL; PROGRAMAÇÃO LINEAR
- Keywords: Label shift; quantification; linear optimization; domain adaptation
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: ACM SIGKDD Conference on Knowledge Discovery and Data Mining - KDD
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
DONYAVI, Zahra et al. Match: a maximum-likelihood approach for classification under label shift. 2025, Anais.. New York: ACM, 2025. Disponível em: https://doi.org/10.1145/3711896.3737040. Acesso em: 31 mar. 2026. -
APA
Donyavi, Z., Li, F., Zhang, Y., Silva, D. F., & Batista, G. E. de A. P. A. (2025). Match: a maximum-likelihood approach for classification under label shift. In Proceedings. New York: ACM. doi:10.1145/3711896.3737040 -
NLM
Donyavi Z, Li F, Zhang Y, Silva DF, Batista GE de APA. Match: a maximum-likelihood approach for classification under label shift [Internet]. Proceedings. 2025 ;[citado 2026 mar. 31 ] Available from: https://doi.org/10.1145/3711896.3737040 -
Vancouver
Donyavi Z, Li F, Zhang Y, Silva DF, Batista GE de APA. Match: a maximum-likelihood approach for classification under label shift [Internet]. Proceedings. 2025 ;[citado 2026 mar. 31 ] Available from: https://doi.org/10.1145/3711896.3737040 - A divergence-based pruning approach for tree selection in continuous data streams
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