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Alcobaça, Edesio
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1

Machine learning unveils composition-property relationships..:

Mastelini, Saulo Martiello ; Cassar, Daniel R. ; Alcobaça, Edesio...
Acta Materialia.  240 (2022)  - p. 118302 , 2022
Link: https://doi.org/10.1016/..
 
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Predicting and interpreting oxide glass properties by machi..:

Cassar, Daniel R. ; Mastelini, Saulo Martiello ; Botari, Tiago...
Ceramics International.  47 (2021)  17 - p. 23958-23972 , 2021
Link: https://doi.org/10.1016/..
 
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3

Explainable Machine Learning Algorithms For Predicting Glas..:

Alcobaça, Edesio ; Mastelini, Saulo Martiello ; Botari, Tiago...
Acta Materialia.  188 (2020)  - p. 92-100 , 2020
Link: https://doi.org/10.1016/..
 
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A meta-learning recommender system for hyperparameter tunin..:

Mantovani, Rafael G. ; Rossi, André L.D. ; Alcobaça, Edesio..
Information Sciences.  501 (2019)  - p. 193-221 , 2019
Link: https://doi.org/10.1016/..
 
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5

Lessons learned from the NeurIPS 2021 MetaDL challenge: Bac..:

Baz, Adrian El ; Ullah, Ihsan ; Alcobaça, Edesio...
http://arxiv.org/abs/2206.08138.  , 2022
Link: http://arxiv.org/abs/220..
 
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6

Machine learning unveils composition-property relationships..:

Mastelini, Saulo M ; Cassar, Daniel R ; Alcobaça, Edesio...
http://arxiv.org/abs/2106.07749.  , 2021
Link: http://arxiv.org/abs/210..
 
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7

Lessons learned from the NeurIPS 2021 MetaDL challenge: Bac..:

El Baz, Adrian ; Ullah, Ihsan ; Alcobaça, Edesio...
info:eu-repo/semantics/altIdentifier/arxiv/2206.08138.  , 2021
Link: https://hal.science/hal-..
 
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8

Lessons learned from the NeurIPS 2021 MetaDL challenge: Bac..:

El Baz, Adrian ; Ullah, Ihsan ; Alcobaça, Edesio...
info:eu-repo/semantics/altIdentifier/arxiv/2206.08138.  , 2021
Link: https://hal.science/hal-..
 
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9

Rethinking Default Values: a Low Cost and Efficient Strateg..:

Mantovani, Rafael Gomes ; Rossi, André Luis Debiaso ; Alcobaça, Edesio...
http://arxiv.org/abs/2008.00025.  , 2020
Link: http://arxiv.org/abs/200..
 
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10

Predicting and interpreting oxide glass properties by machi..:

Cassar, Daniel R ; Mastelini, Saulo Martiello ; Botari, Tiago...
http://arxiv.org/abs/2009.03194.  , 2020
Link: http://arxiv.org/abs/200..
 
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11

A meta-learning recommender system for hyperparameter tunin..:

Mantovani, Rafael G ; Rossi, Andre L. D ; Alcobaca, Edesio..
Information Sciences.  , 2019
Link: http://hdl.handle.net/11..
 
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12

A meta-learning recommender system for hyperparameter tunin..:

Mantovani, Rafael G ; Rossi, André LD ; Alcobaça, Edesio..
ISSN:0020-0255.  , 2019
Link: http://repository.tue.nl..
 
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13

A meta-learning recommender system for hyperparameter tunin..:

Mantovani, Rafael Gomes ; Rossi, André Luis Debiaso ; Alcobaça, Edesio..
http://arxiv.org/abs/1906.01684.  , 2019
Link: http://arxiv.org/abs/190..
 
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14

A meta-learning recommender system for hyperparameter tunin..:

Mantovani, Rafael G ; Rossi, André L.D ; Alcobaça, Edesio..
Mantovani , R G , Rossi , A L D , Alcobaça , E , Vanschoren , J & de Carvalho , A C P L F 2019 , ' A meta-learning recommender system for hyperparameter tuning : Predicting when tuning improves SVM classifiers ' , Information Sciences , vol. 501 , pp. 193-221 . https://doi.org/10.1016/j.ins.2019.06.005.  , 2019
Link: https://research.tue.nl/..
 
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15

Transfer Learning for Algorithm Recommendation:

Pereira, Gean Trindade ; Santos, Moisés dos ; Alcobaça, Edesio..
http://arxiv.org/abs/1910.07012.  , 2019
Link: http://arxiv.org/abs/191..
 
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