A literature review on automated machine learning (2026)
- Authors:
- USP affiliated authors: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; ALCOBAÇA NETO, EDESIO PINTO DE SOUZA - ICMC
- Unidade: ICMC
- DOI: 10.1007/s10462-025-11397-2
- Subjects: APRENDIZADO COMPUTACIONAL; REVISÃO SISTEMÁTICA
- Keywords: Automated machine learning; AutoML; Metalearning; Hyperparameter optimization; Transfer learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Artificial Intelligence Review
- ISSN: 0269-2821
- Volume/Número/Paginação/Ano: v. 59, n. 1, p. 1-39, Jan. 2026
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: cc-by
-
ABNT
ALCOBAÇA, Edesio e CARVALHO, André Carlos Ponce de Leon Ferreira de. A literature review on automated machine learning. Artificial Intelligence Review, v. 59, n. Ja 2026, p. 1-39, 2026Tradução . . Disponível em: https://doi.org/10.1007/s10462-025-11397-2. Acesso em: 03 dez. 2025. -
APA
Alcobaça, E., & Carvalho, A. C. P. de L. F. de. (2026). A literature review on automated machine learning. Artificial Intelligence Review, 59( Ja 2026), 1-39. doi:10.1007/s10462-025-11397-2 -
NLM
Alcobaça E, Carvalho ACP de LF de. A literature review on automated machine learning [Internet]. Artificial Intelligence Review. 2026 ; 59( Ja 2026): 1-39.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1007/s10462-025-11397-2 -
Vancouver
Alcobaça E, Carvalho ACP de LF de. A literature review on automated machine learning [Internet]. Artificial Intelligence Review. 2026 ; 59( Ja 2026): 1-39.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1007/s10462-025-11397-2 - Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
- Boosting meta-learning with simulated data complexity measures
- MFE: towards reproducible meta-feature extraction
- Machine learning unveils composition-property relationships in chalcogenide glasses
- Predicting and interpreting oxide glass properties by machine learning using large datasets
- Explainable machine learning algorithms for predicting glass transition temperatures
- Reduction strategies for hierarchical multi-label classification in protein function prediction
- Intelligent-guided adaptive search for the maximum covering location problem
- Clus-DTI: improving decision-tree classification with a clustering-based decision-tree induction algorithm
- A density-based clustering approach for behavior change detection in data streams
Informações sobre o DOI: 10.1007/s10462-025-11397-2 (Fonte: oaDOI API)
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