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Refinado por: autor: Alcobaça, E remover assunto: Aprendizado Computacional remover
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1
A meta-learning recommender system for hyperparameter tuning predicting when tuning improves SVM classifiers
A meta-learning recommender system for hyperparameter tuning predicting when tuning improves SVM classifiers
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A meta-learning recommender system for hyperparameter tuning predicting when tuning improves SVM classifiers

Rafael G Mantovani André Luis Debiaso Rossi; Edesio Alcobaça; Joaquin Vanschoren; André Carlos Ponce de Leon Ferreira de Carvalho

Information Sciences New York : Elsevier v. 501, p. 193-221, Oct. 2019

New York 2019

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-2948367 )(Acessar)

2
Boosting meta-learning with simulated data complexity measures
Boosting meta-learning with simulated data complexity measures
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Boosting meta-learning with simulated data complexity measures

Luís Paulo Faina Garcia Adriano Rivolli; Edesio Alcobaça; Ana Carolina Lorena; André Carlos Ponce de Leon Ferreira de Carvalho

Intelligent Data Analysis Amsterdam : IOS Press v. 24, n. 5, p. 1011-1028, 2020

Amsterdam 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3011249 )(Acessar)

3
Dimensionality reduction for the algorithm recommendation problem
Dimensionality reduction for the algorithm recommendation problem
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Dimensionality reduction for the algorithm recommendation problem

Edesio Alcobaça Rafael G Mantovani; André Luis Debiaso Rossi; André Carlos Ponce de Leon Ferreira de Carvalho; Brazilian Conference on Intelligent Systems - BRACIS (7. 2018 São Paulo)

Proceedings Piscataway : IEEE, 2018

Piscataway IEEE 2018

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-2917418 )(Acessar)

4
MFE towards reproducible meta-feature extraction
MFE towards reproducible meta-feature extraction
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MFE towards reproducible meta-feature extraction

Edesio Alcobaça Felipe Alves Siqueira; Adriano Rivolli; Luís Paulo Faina Garcia; Jefferson Tales Oliva; André Carlos Ponce de Leon Ferreira de Carvalho

Journal of Machine Learning Research Brookline : Microtome v. 21, p. 1-5, 2020

Brookline 2020

Acesso online. A biblioteca também possui exemplares impressos.

5
Explainable machine learning algorithms for predicting glass transition temperatures
Explainable machine learning algorithms for predicting glass transition temperatures
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Explainable machine learning algorithms for predicting glass transition temperatures

Edesio Alcobaça Saulo Martiello Mastelini; Tiago Botari; Bruno Almeida Pimentel; Daniel Roberto Cassar; André Carlos Ponce de Leon Ferreira de Carvalho; Edgar Dutra Zanotto

Acta Materialia Kidlington : Pergamon : Elsevier v. 188, p. 92-100, Apr. 2020

Kidlington 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-2987745 )(Acessar)

6
Predicting and interpreting oxide glass properties by machine learning using large datasets
Predicting and interpreting oxide glass properties by machine learning using large datasets
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Predicting and interpreting oxide glass properties by machine learning using large datasets

Daniel Roberto Cassar Saulo Martiello Mastelini; Tiago Botari; Edesio Alcobaça; André Carlos Ponce de Leon Ferreira de Carvalho; Edgar Dutra Zanotto

Ceramics International Kidlington v. 47, n. 17, p. 23958-23972, Sep. 2021

Kidlington 2021

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3032587 )(Acessar)

7
Machine learning unveils composition-property relationships in chalcogenide glasses
Machine learning unveils composition-property relationships in chalcogenide glasses
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Machine learning unveils composition-property relationships in chalcogenide glasses

Saulo Martiello Mastelini Daniel Roberto Cassar; Edesio Alcobaça; Tiago Botari; André Carlos Ponce de Leon Ferreira de Carvalho; Edgar Dutra Zanotto

Acta Materialia Kidlington v. 240, p. 1-13, Nov. 2022

Kidlington 2022

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3096526 )(Acessar)

8
End-to-end data science (Pajé)
End-to-end data science (Pajé)
Material Type:
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End-to-end data science (Pajé)

Edesio Alcobaça Davi Pereira dos Santos; Moises Rocha dos Santos; Gean Trindade Pereira; Rafael Gomes Mantovani; Luís Paulo Faina Garcia; Saulo Martiello Mastelini; André Carlos Ponce de Leon Ferreira de Carvalho; Seminário de Inteligência Artificial Aplicada à Indústria do Petróleo (1st. 2019 Rio de Janeiro)

Resumos Rio de Janeiro : ABGP, 2019

Rio de Janeiro ABGP 2019

Acesso online. A biblioteca também possui exemplares impressos.

9
Lessons learned from the NeurIPS 2021 MetaDL challenge backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Lessons learned from the NeurIPS 2021 MetaDL challenge backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
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Lessons learned from the NeurIPS 2021 MetaDL challenge backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

Adrian El Baz Ihsan Ullah; Edesio Alcobaça; André Carlos Ponce de Leon Ferreira de Carvalho; Hong Chen; Fábio Ferreira; Henry Gouk; Chaoyu Guan; Isabelle Guyon; Timothy Hospedales; Shell Hu; Mike Huisman; Frank Hutter; Zhengying Liu; Felix Mohr; Ekrem Öztürk; Jan N. van Rijn; Haozhe Sun; Xin Wang; Wenwu Zhu; Conference on Neural Information Processing Systems - NeurIPS (35. 2021 Online); NeurIPS 2021 Competitions and Demonstrations Track (2021 Online)

Proceedings of Machine Learning Research : PMLR Brookline : Microtome Publishing v. 176, p. 80-96, 2022

Brookline Microtome Publishing 2022

Acesso online. A biblioteca também possui exemplares impressos.

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