Using both latent and supervised shared topics for multitask learning (2013)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-642-40991-2_24
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: Springer-Verlag
- Publisher place: Berlin
- Date published: 2013
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 8189, p. 369-384, 2013
- Conference titles: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ACHARYA, Ayan et al. Using both latent and supervised shared topics for multitask learning. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-40991-2_24. Acesso em: 28 dez. 2025. , 2013 -
APA
Acharya, A., Rawal, A., Mooney, R. J., & Hruschka, E. R. (2013). Using both latent and supervised shared topics for multitask learning. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. doi:10.1007/978-3-642-40991-2_24 -
NLM
Acharya A, Rawal A, Mooney RJ, Hruschka ER. Using both latent and supervised shared topics for multitask learning [Internet]. Lecture Notes in Artificial Intelligence. 2013 ; 8189 369-384.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1007/978-3-642-40991-2_24 -
Vancouver
Acharya A, Rawal A, Mooney RJ, Hruschka ER. Using both latent and supervised shared topics for multitask learning [Internet]. Lecture Notes in Artificial Intelligence. 2013 ; 8189 369-384.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1007/978-3-642-40991-2_24 - An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks
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Informações sobre o DOI: 10.1007/978-3-642-40991-2_24 (Fonte: oaDOI API)
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