Portfolio item number 1
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Posts by Collection
portfolio
Portfolio item number 2
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publications
Dimensionality reduction for the algorithm recommendation problem
Alcobaça, E., Mantovani, R. G., Rossi, A. L., & De Carvalho, A. C. (2018, October). Dimensionality reduction for the algorithm recommendation problem. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 318-323). IEEE.
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
Mantovani, R. G., Rossi, A. L., Alcobaça, E., Vanschoren, J., & de Carvalho, A. C. (2019). A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers. Information Sciences, 501, 193-221.
Transfer learning for algorithm recommendation
Pereira, G. T., Santos, M. D., Alcobaça, E., Mantovani, R., & Carvalho, A. (2019). Transfer learning for algorithm recommendation. arXiv preprint arXiv:1910.07012.
Explainable machine learning algorithms for predicting glass transition temperatures
Alcobaça, E., Mastelini, S. M., Botari, T., Pimentel, B. A., Cassar, D. R., de Leon Ferreira, A. C. P., & Zanotto, E. D. (2020). Explainable machine learning algorithms for predicting glass transition temperatures. Acta Materialia
MFE: Towards reproducible meta-feature extraction
Alcobaça, E., Siqueira, F., Rivolli, A., Garcia, L. P. F., Oliva, J. T., & de Carvalho, A. C. (2020). MFE: Towards reproducible meta-feature extraction. Journal of Machine Learning Research , 21, 111-1.
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Mantovani, R. G., Rossi, A. L. D., Alcobaça, E., Gertrudes, J. C., Junior, S. B., & de Carvalho, A. C. P. D. L. F. (2020). Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters. arXiv preprint arXiv:2008.00025.
Boosting meta-learning with simulated data complexity measures
Garcia, L. P., Rivolli, A., Alcobaça, E., Lorena, A. C., & de Carvalho, A. C. (2020). Boosting meta-learning with simulated data complexity measures. Intelligent Data Analysis, 24(5), 1011-1028.
Predicting and interpreting oxide glass properties by machine learning using large datasets
Cassar, D. R., Mastelini, S. M., Botari, T., Alcobaça, E., de Carvalho, A. C., & Zanotto, E. D. (2021). Predicting and interpreting oxide glass properties by machine learning using large datasets. Ceramics International.
Machine learning unveils composition-property relationships in chalcogenide glasses
Mastelini, S. M., Cassar, D. R., Alcobaça, E., Botari, T., de Carvalho, A. C., & Zanotto, E. D. (2021). Machine learning unveils composition-property relationships in chalcogenide glasses. arXiv preprint arXiv:2106.07749.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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