Edesio Alcobaça
Edesio Alcobaça
PhD candidate, Universidade de São Paulo
Geverifieerd e-mailadres voor usp.br
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Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures
E Alcobaça, SM Mastelini, T Botari, BA Pimentel, DR Cassar, ...
Acta Materialia 188, 92-100, 2020
102020
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
RG Mantovani, ALD Rossi, E Alcobaça, J Vanschoren, AC de Carvalho
Information Sciences 501, 193-221, 2019
10*2019
Predicting thermal, mechanical, and optical properties of oxide glasses by machine learning using large datasets
DR Cassar, SM Mastelini, T Botari, E Alcobaça, AC de Carvalho, ...
arXiv preprint arXiv:2009.03194, 2020
12020
MFE: Towards reproducible meta-feature extraction
E Alcobaça, F Siqueira, A Rivolli, LPF Garcia, JT Oliva, AC de Carvalho
Journal of Machine Learning Research 21 (111), 1-5, 2020
12020
Transfer Learning for Algorithm Recommendation
GT Pereira, M Santos, E Alcobaça, R Mantovani, A Carvalho
arXiv preprint arXiv:1910.07012, 2019
12019
Dimensionality reduction for the algorithm recommendation problem
E Alcobaça, RG Mantovani, ALD Rossi, AC De Carvalho
2018 7th Brazilian Conference on Intelligent Systems (BRACIS), 318-323, 2018
12018
Rethinking Defaults Values: a Low Cost and Efficient Strategy to Define Hyperparameters
RG Mantovani, ALD Rossi, E Alcobaça, JC Gertrudes, SB Junior, ...
arXiv preprint arXiv:2008.00025, 2020
2020
Boosting meta-learning with simulated data complexity measures
LPF Garcia, A Rivolli, E Alcoba, AC Lorena, AC de Carvalho
Intelligent Data Analysis 24 (5), 1011-1028, 2020
2020
SUPPLEMENTARY MATERIAL TO" EXPLAINABLE MACHINE LEARNING ALGORITHMS TO PREDICT GLASS TRANSITION TEMPERATURE
E Alcobaça, SM Mastelini, T Botari, BA Pimentel
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Artikelen 1–9